Emergency energy-saving method, device, equipment and medium

Abstract

The invention relates to an emergency energy-saving method, device and equipment and a medium, and effectively solves the technical problems that service feature analysis is not available, key services cannot be effectively identified and protected, the sensitivity and precision of energy consumption adjustment are insufficient, and the energy-saving and consumption-reducing effects are greatly limited. The emergency energy-saving method comprises the following steps: acquiring historical business data and energy consumption data of a server; based on the historical business data and the energy consumption data, predicting the business operation condition of the server within a certain time period; and when the server is in an energy emergency, determining a target service needing to be shut down based on the service operation condition and the service importance degree, and shutting down the target service.

Description

Technical Field

[0001] The present disclosure relates to the technical field of server energy consumption, and in particular to an emergency energy-saving method, device, equipment and medium. Background Art

[0002] The existing technical solutions mainly achieve energy saving effects by dividing servers into business operation groups and non-business operation groups, limiting the average power consumption of the business operation groups, and setting the non-business operation groups to shut down. However, the existing energy-saving solutions do not have business feature analysis, cannot effectively identify and protect key businesses, and lack sensitivity and accuracy in energy consumption regulation, which greatly limits the effectiveness of energy saving and consumption reduction. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides an emergency energy-saving method, device, equipment and medium, which effectively solves the technical problems of lack of business feature analysis, inability to effectively identify and protect key businesses, insufficient sensitivity and accuracy in energy consumption regulation, and greatly limiting the effectiveness of energy saving and consumption reduction.

[0004] In a first aspect, an embodiment of the present disclosure provides an emergency energy saving method, the method comprising:

[0005] Obtain historical business data and energy consumption data of the server;

[0006] Predict the business operation status of the server within a certain period of time based on historical business data and energy consumption data;

[0007] When the server energy is in a critical condition, the target business that needs to be shut down is determined based on the business operation status and business importance, and the target business is shut down.

[0008] In a possible implementation, in the method provided by the embodiment of the present invention, predicting the service operation status of the server within a certain period of time based on historical service data and energy consumption data includes:

[0009] Establish energy consumption model based on historical business data and energy consumption data;

[0010] Predict business operation status based on energy consumption models.

[0011] In a possible implementation, in the method provided in the embodiment of the present invention, an energy consumption model is established based on historical business data and energy consumption data, including:

[0012] Determine the service type of the server based on historical service data;

[0013] Determine the power consumption curve of the server based on the energy consumption data;

[0014] Analyze the business type and power consumption curve to obtain the energy consumption model.

[0015] In a possible implementation, in the method provided by the embodiment of the present invention, when the server energy is in emergency, the target business to be shut down is determined based on the business operation status and the business importance, and before shutting down the target business, the method further includes:

[0016] Use the business type and pre-defined business energy consumption guarantee level table to determine the importance of the business.

[0017] In a second aspect, an embodiment of the present disclosure provides an emergency energy-saving device, the device comprising:

[0018] An acquisition unit, used to acquire historical service data and energy consumption data of the server;

[0019] A prediction unit, used to predict the service operation status of the server within a certain period of time based on historical service data and energy consumption data;

[0020] The processing unit is used to determine the target business that needs to be shut down based on the business operation status and business importance when the server energy is in emergency, and shut down the target business.

[0021] In a possible implementation manner, in the device provided by the embodiment of the present invention, the prediction unit is specifically used for:

[0022] Establish energy consumption model based on historical business data and energy consumption data;

[0023] Predict business operation status based on energy consumption models.

[0024] In a possible implementation manner, in the device provided by the embodiment of the present invention, the prediction unit is specifically used for:

[0025] Determine the service type of the server based on historical service data;

[0026] Determine the power consumption curve of the server based on the energy consumption data;

[0027] Analyze the business type and power consumption curve to obtain the energy consumption model.

[0028] In a possible implementation manner, in the device provided by the embodiment of the present invention, the processing unit is further used for:

[0029] Use the business type and pre-defined business energy consumption guarantee level table to determine the importance of the business.

[0030] In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

[0031] Memory;

[0032] Processor; and

[0033] Computer programs;

[0034] The computer program is stored in the memory and is configured to be executed by the processor to implement the emergency energy saving method as described above.

[0035] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the emergency energy saving method as described above are implemented.

[0036] The present disclosure provides an emergency energy saving method, including:

[0037] First, the historical business data and energy consumption data of the server are obtained, and then the business operation status of the server within a certain period of time is predicted based on the historical business data and the energy consumption data. Finally, when the server energy is in an emergency, the target business to be shut down is determined based on the business operation status and the importance of the business, and the target business is shut down. By applying the emergency energy-saving method provided by the present disclosure, it is possible to partially shut down / limit the current or reduce the frequency to maintain important businesses in the case of emergency power supply, thereby maximizing the energy saving and consumption reduction of the server in an emergency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A schematic diagram of a flow chart of an emergency energy saving method provided by an embodiment of the present disclosure;

[0041] Figure 2 A schematic diagram of the structure of an emergency energy-saving device provided in an embodiment of the present disclosure;

[0042] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0045] 1. In the embodiments of the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0046] The existing technical solution mainly achieves energy saving by dividing the server into business operation group and non-business operation group, limiting the average power consumption of the business operation group and setting the non-business operation group to shut down. The process is as follows:

[0047] First, obtain the upper limit average power consumption value of each x86 server, obtain detailed business operation information from the resource scheduling system, and then dynamically establish business operation energy consumption management group and non-business operation energy consumption management group, update and operate the host information of business operation energy consumption management group and non-business operation energy consumption management group, and finally set the energy-saving strategy to set the average power consumption upper limit and limit the time period for the business operation group to save energy, and set a shutdown strategy for the non-business operation group, so as to achieve energy-saving effects without affecting the business.

[0048] In summary, existing energy-saving solutions lack business feature analysis, are unable to effectively identify and protect key businesses, and lack sensitivity and accuracy in energy consumption regulation, which greatly limits the effectiveness of energy conservation and consumption reduction.

[0049] Figure 1 A schematic diagram of a flow chart of an emergency energy saving method provided by an embodiment of the present disclosure, specifically including the following steps: Figure 1 The following steps S101 to S103 are shown:

[0050] S101. Obtain historical service data and energy consumption data of a server.

[0051] In the specific implementation, for historical business data, technical means (such as traffic monitoring tools) are used to collect and analyze the business data of the resource pool in recent months. These data include the number of visits by time period, the application to which they belong, the frequency of data access, etc., which provide a reference for the subsequent determination of business types.

[0052] Preferably, historical business data under emergency conditions are eliminated to reduce inaccurate estimates due to special reasons.

[0053] For energy consumption data, energy consumption data is obtained through server monitoring systems or sensors (such as IPMI or SNMP, etc.), which are required to be as accurate as possible. Server power consumption data, business request volume, etc. are collected at a granularity of minutes, and the power consumption of the server in different time periods and business types is recorded, including power consumption values ​​during normal operation, peak power consumption values, etc.

[0054] S102: Predict the service operation status of the server within a certain period of time based on historical service data and energy consumption data.

[0055] During the specific implementation, an energy consumption model is first established based on historical business data and energy consumption data, and then the business operation status is predicted based on the energy consumption model.

[0056] To establish an energy consumption model, first determine the service type of the server based on historical service data, then determine the power consumption curve of the server based on the energy consumption data, and finally analyze the service type and power consumption curve to obtain the energy consumption model. Specifically:

[0057] For historical business data, technical means (such as performance monitoring tools) are used to analyze key information such as the server CPU, memory, disk, concurrency, network bandwidth utilization, response time, etc. during the business operation based on historical business data to determine different business types. In one embodiment, the business can be divided into computing intensive, I / O intensive, memory intensive, concurrent connection intensive, network bandwidth intensive, disk space intensive, response time sensitive, etc. Some complex businesses may have several business characteristics at the same time. Each business type has different resource requirements and characteristics, and server energy consumption is allocated in a targeted manner.

[0058] For example, compute-intensive businesses include businesses that require a large amount of CPU resources to perform compute-intensive tasks. For example, tasks such as data analysis, model training, and complex algorithm execution. Monitor the CPU utilization of the server. When the CPU utilization is continuously higher than 80%, it can be judged as a CPU-intensive business. Analyze the volatility and peak values ​​of CPU utilization in historical data. High volatility and peak values, with peak values ​​higher than 80% for more than 1 / 3 of the time every day, are CPU-intensive tasks.

[0059] I / O-intensive services include services that have high demands on disk I / O operations. For example, tasks such as database operations, file reading and writing, and log writing. When monitoring business loads, if the CPU usage rate is continuously below 40% and the disk I / O usage rate exceeds 80%, it can be judged as an I / O-intensive business.

[0060] Memory-intensive services include services that have a high demand for a large amount of memory resources. For example, memory databases, cache services, image processing and other tasks. Monitor the memory usage of the service load server. When the memory usage rate is higher than 80% and the server memory page replacement rate is higher than 50% per unit time, the service has frequent memory replacement, indicating that the service has a large demand for memory and can be judged as a memory-intensive service.

[0061] Concurrent connection-intensive services include services that need to handle a large number of concurrent connections at the same time. For example, services such as Web servers and message queues, which have hundreds to thousands of concurrent connections per second, can be considered concurrent-intensive services. Determining whether a service can be defined as concurrent-intensive depends on multiple factors, including server hardware performance, network bandwidth, etc.

[0062] Network bandwidth-intensive services include services that require a large amount of network bandwidth resources. For example, services such as real-time video calls and online live broadcasts. Low-bandwidth services require bandwidths between hundreds of Kbps and several Mbps. These services may be some text-based information transmission, data transmission of lightweight applications, etc. Medium-bandwidth services: services that require bandwidths between several Mbps and tens of Mbps. These services may include some online video viewing, file downloads, etc. High-bandwidth services require bandwidths between tens of Mbps and hundreds of Mbps. These services may include high-definition video streaming, online games, etc. Ultra-high-bandwidth services require bandwidths above hundreds of Mbps. These services may include 4K / 8K video streaming, large-scale data transmission, etc.

[0063] Disk space intensive services include services that require a lot of disk space to store data. For example, big data storage, log storage and other tasks. Monitor the server's disk space usage growth rate and backup replacement rate. When the disk weekly growth rate exceeds 20% or the disk backup conversion rate exceeds 20%, the service is considered disk space intensive.

[0064] Response time-sensitive services include those that have high requirements for response time and concurrent request response speed, and services usually need to respond quickly to user requests. For example, financial transactions, real-time communication services, online game services, etc., analyze the request response time and concurrent request number in historical business data, and when the request response time is short and the concurrent request number is high, it can be identified as a response time-sensitive business.

[0065] For energy consumption data, including daily average energy consumption, daily maximum energy consumption, daily minimum energy consumption, etc., a server power consumption curve is established through a large amount of energy consumption data. The statistical data is divided into periods of hours / days / weeks / months, and used as fitting periods to help establish an energy consumption model, fit the data rules, and judge abnormal values ​​through standard deviation. If the difference between a data point and the average value exceeds a certain standard deviation threshold, it can be considered an abnormal value.

[0066] Standard deviation = sqrt((Σ(daily energy consumption - average energy consumption)^2) / (total number of days - 1))

[0067] Outlier threshold = average energy consumption + 2*standard deviation.

[0068] The collected energy consumption data is analyzed in correlation with the service types carried by the server to obtain the correlation between the service characteristics determined by the service type and the server energy consumption data. The energy consumption model is used to capture the energy consumption trend and seasonality of various service types running on the server, thereby predicting the service operation status, service energy consumption peak, trough, and average energy consumption in the future, and then calculating the sustainable operation time of each service under emergency limited power supply conditions. The core formula is:

[0069] y(t)=g(t)+h(t)+∈t

[0070] in:

[0071] y(t) is the observed energy consumption data at time point "t".

[0072] g(t) is the energy consumption trend component, which represents the long-term change trend of the data.

[0073] h(t) is the holiday energy consumption effect component, which indicates the impact of holidays on the data.

[0074] ∈t is a random error term, representing random fluctuations that cannot be explained by the model.

[0075] The trend component g(t) can be expressed as:

[0076]

[0077] The basis of g(t) is piecewise linear functions, which are used to describe the energy consumption trends of different business types in different time periods. Piecewise linear functions mean that the trend of energy consumption data can change in different intervals in time.

[0078] a j represents the energy consumption coefficient of the piecewise linear function, and f j (t) is the basic function, which is used to describe the trend of a specific time period. j(t) According to the energy consumption data collected by the network cloud, a periodic function based on day, week, or month is selected for each time period. j (t) = sinθ.

[0079] b k Represents the mutation point coefficient in the piecewise linear function. The mutation point is used to indicate that the trend of the data suddenly changes at a certain point in time, that is, the data changes from one trend to another. This helps to capture possible sudden changes in energy consumption data over time.

[0080] The overall structure of g(t) is a number of piecewise linear functions a j f j (t), plus the mutation point b k f k This construction method can more flexibly adapt to trend changes in different time periods, is not limited to a single function model, meets the energy consumption data changes with a time period, and fits the business energy consumption trend more flexibly and comprehensively.

[0081] The holiday component h(t) can be expressed as:

[0082]

[0083] Among them, δ and ε are coefficients representing the increase in energy consumption, which are adjusted according to the actual impact of data and holidays. When forecasting, the energy consumption forecasting model will take this holiday effect into account so that the forecast on the holiday day captures their impact on business energy consumption data. In this way, the model can more accurately predict data changes during holidays. The size and direction (increase or decrease) of the holiday effect needs to be determined based on actual conditions and historical data.

[0084] The random error term ∈t can be expressed as: We cannot directly know the specific value of ∈t, but we can perform statistical analysis on the predicted and observed values ​​of business energy consumption. According to the statistical analysis of network cloud energy consumption, ∈t can be set to a normally distributed random variable with a mean of zero. The enterprise random variable can be set according to the observed data and predictions, and the embodiments of the present disclosure do not limit this.

[0085] The energy consumption curve describes the power consumption of the server under different service characteristics. Through the energy consumption model, the running time of the server when carrying a specified service can be calculated to select the best service migration in emergency situations.

[0086] S103: When the server energy is in emergency, determine the target business that needs to be shut down based on the business operation status and business importance, and shut down the target business.

[0087] When implementing it, first use the business type and the pre-established business energy consumption guarantee level table to determine the importance of the business. Specifically:

[0088] Determine the importance of business energy consumption according to the characteristics of the enterprise business, and determine the energy consumption guarantee level for each business type. The core business is more important, so a high-level energy consumption guarantee is set, while the secondary business is less important, so a low-level energy consumption guarantee is set. By formulating a business importance guarantee level table, the business energy consumption guarantee rules can be reasonably determined.

[0089] Still using the above example, each type needs to formulate a business basic energy protection level based on its own business characteristics.

[0090] For computing-intensive services, which are highly real-time and affect a large number of services per unit time, and have a high security level, it is necessary to increase the energy security level and allocate more energy consumption, while reducing the CPU frequency to ensure business continuity.

[0091] For I / O intensive services, the service flow time is long and the impact on the business volume per unit time is small. It is necessary to lower the energy consumption guarantee level, reduce energy consumption allocation, and shut down some storage devices.

[0092] For memory-intensive businesses, which have fast turnover, affect more businesses per unit time, and have a high security level, it is necessary to increase the energy security level and allocate more energy consumption. At the same time, the memory frequency should be reduced to ensure business continuity.

[0093] For concurrent connection-intensive services, concurrent services are time-intensive, with a high security level during peak periods and a low security level during valley periods. It is necessary to increase or decrease energy consumption allocation according to the concurrent peak and valley periods.

[0094] For network bandwidth-intensive services, bandwidth-based services involve large amounts of data transmission. In emergency situations, large-scale data transmission should be reduced and traffic should be reserved for small and fast services to achieve more value. It is necessary to reduce energy consumption levels, limit or shut down server port bandwidth, and reduce business volume.

[0095] For disk space intensive services, the business turnover time is long and the impact on business volume per unit time is small. It is necessary to lower the energy consumption guarantee level, reduce energy consumption allocation, and shut down some storage devices.

[0096] For response time-sensitive businesses, there are many businesses flowing per unit time, the business value is high, and the energy consumption guarantee level is high, which requires maintaining high equipment performance and a large number of server devices.

[0097] Then, the energy-saving scheduling plan is quantified and implemented. The above steps involve multiple parameters. Some data are objectively calculated, such as business volume historical trends / future forecasts, business type determination, and the relationship between energy consumption and business characteristics. Some data belong to business parameters, which depend on the specific enterprise, such as the business energy consumption guarantee level table, which can guarantee energy consumption in different stages, quantify the energy-saving priority in emergency situations, accurately execute all / partial power-off processing of low-priority services in advance, and implement current limiting or energy-saving measures for important services.

[0098] In an example, the stages are as follows:

[0099] During the phase of shutting down non-energy-saving services, energy saving is shut down for the lowest-level services in turn according to the internal basic energy-saving level and the energy-saving level of the business classification, and the servers are powered off.

[0100] During the energy protection business phase with duration priority, for businesses with the same protection level, protection will be provided to businesses with longer running time based on the energy consumption model prediction time. For businesses with short predicted running time or entering the trough, servers will be reduced or shut down, and servers will be powered off.

[0101] During the phase of ensuring the availability of important businesses, server performance is reduced for important businesses to keep the businesses available while saving energy to the maximum extent possible, limiting business traffic and reducing server performance.

[0102] After the above steps are set up, analysis can be automatically performed and energy-saving plans can be executed in emergency situations to maximize the use of electricity consumption in emergency situations, ensure that important businesses can run for as long as possible, and minimize business losses.

[0103] Servers are key infrastructure supporting network cloud computing. In emergency power supply situations, such as power outages or power failures, traditional emergency power supply solutions may be difficult to meet the needs of keeping key services alive. A new method is needed to analyze / control server status according to business characteristics, ensure the continuous operation of key businesses as much as possible, and minimize business losses. The system that implements this method can be composed of the following modules: data entry, server indicator collector, business classifier, business energy protection assessment module, business energy consumption module, emergency energy consumption scheduler, etc.

[0104] Among them, the data entry device receives and cleans historical business data uploaded by users to prepare for business classification. The server indicator collector is used to collect indicators such as server CPU, memory, disk, concurrency, bandwidth utilization, application time, etc. during the business operation period. The business classifier combines the data entered by the entry device and the data collected by the collector to jointly determine the business type and store it in the database. The business energy protection assessment module combines the internal business level of the enterprise and the data saved by the classifier to calculate the business energy protection level and determine the energy protection measures. The server energy consumption collector continuously collects server energy consumption data, especially energy consumption data in emergency situations. The business energy consumption module, combined with business type and energy consumption, establishes a model to predict business energy consumption trends and provide a quantitative basis for energy consumption scheduling. The emergency energy consumption scheduler, combined with the business energy protection level, business energy protection measures, and business energy consumption trends, dynamically outputs the best energy-saving scheduling plan in emergency situations.

[0105] This technical solution combines business characteristics to effectively distinguish business types, evaluates importance based on business types, establishes an energy consumption model based on business characteristics and energy consumption correlation, and accurately calculates the operating time of characteristic businesses under emergency power supply conditions. Ultimately, it prioritizes important businesses through energy consumption guarantee levels, shuts down secondary / unimportant businesses, partially shuts down / limits current, or reduces frequency to maintain important businesses, thereby maximizing server energy saving and consumption reduction in emergency situations.

[0106] Figure 2 The schematic diagram of the structure of the emergency energy-saving device provided in the embodiment of the present disclosure is as follows. The emergency energy-saving device 200 provided in the embodiment of the present disclosure can execute the processing flow provided in the above-mentioned emergency energy-saving method embodiment, such as Figure 2 As shown, the emergency energy saving device 200 includes an acquisition unit 201, a prediction unit 202 and a processing unit 203, wherein:

[0107] An acquisition unit 201 is used to acquire historical service data and energy consumption data of a server;

[0108] A prediction unit 202, used to predict the service operation status of the server within a certain period of time based on historical service data and energy consumption data;

[0109] The processing unit 203 is used to determine the target business that needs to be shut down based on the business operation status and the business importance when the server energy is in emergency, and shut down the target business.

[0110] In a possible implementation manner, in the device provided by the embodiment of the present invention, the prediction unit 202 is specifically used for:

[0111] Establish energy consumption model based on historical business data and energy consumption data;

[0112] Predict business operation status based on energy consumption models.

[0113] In a possible implementation manner, in the device provided by the embodiment of the present invention, the prediction unit 202 is specifically used for:

[0114] Determine the service type of the server based on historical service data;

[0115] Determine the power consumption curve of the server based on the energy consumption data;

[0116] Analyze the business type and power consumption curve to obtain the energy consumption model.

[0117] In a possible implementation manner, in the device provided by the embodiment of the present invention, the processing unit 203 is further configured to:

[0118] Use the business type and pre-defined business energy consumption guarantee level table to determine the importance of the business.

[0119] Figure 2 The emergency energy-saving device of the illustrated embodiment can be used to implement the technical solution of the above-mentioned method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0120] In addition, combined Figure 1-Figure 2 The emergency energy saving method and device described in the embodiments of the present application can be implemented by an electronic device. Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0121] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303 to implement the emergency energy saving method of the embodiment described in the present disclosure. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0122] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0123] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart, thereby implementing the voice control method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0124] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0125] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0126] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0127] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0128] Obtain historical business data and energy consumption data of the server;

[0129] Predict the business operation status of the server within a certain period of time based on historical business data and energy consumption data;

[0130] When the server energy is in a critical condition, the target business that needs to be shut down is determined based on the business operation status and business importance, and the target business is shut down.

[0131] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0132] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0133] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a 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. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0134] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0135] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0136] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0137] The present disclosure provides an emergency energy saving method, including:

[0138] First, the historical business data and energy consumption data of the server are obtained, and then the business operation status of the server within a certain period of time is predicted based on the historical business data and the energy consumption data. Finally, when the server energy is in an emergency, the target business to be shut down is determined based on the business operation status and the importance of the business, and the target business is shut down. By applying the emergency energy-saving method provided by the present disclosure, it is possible to partially shut down / limit the current or reduce the frequency to maintain important businesses in the case of emergency power supply, thereby maximizing the energy saving and consumption reduction of the server in an emergency.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An emergency energy saving method, characterized in that: The method comprises: Obtain historical business data and energy consumption data of the server; Predicting the service operation status of the server within a certain period of time based on the historical service data and the energy consumption data; When the server energy is in emergency, the target business that needs to be shut down is determined based on the business operation status and the business importance, and the target business is shut down.

2. The method according to claim 1, characterized in that The predicting the service operation status of the server within a certain period of time based on the historical service data and the energy consumption data includes: Establishing an energy consumption model based on the historical business data and the energy consumption data; The business operation status is predicted according to the energy consumption model.

3. The method according to claim 2, characterized in that The establishing of the energy consumption model based on the historical business data and the energy consumption data includes: Determining a service type of the server based on the historical service data; Determine a power consumption curve of the server according to the energy consumption data; The service type and the power consumption curve are analyzed to obtain the energy consumption model.

4. The method according to claim 3, characterized in that When the server energy is in emergency, the target business to be shut down is determined based on the business operation status and the business importance, and before shutting down the target business, the method further includes: The importance of the business is determined by using the business type and a pre-defined business energy consumption guarantee level table.

5. An emergency energy-saving device, characterized in that: The device comprises: An acquisition unit, used to acquire historical service data and energy consumption data of the server; A prediction unit, configured to predict the service operation status of the server within a certain period of time based on the historical service data and the energy consumption data; The processing unit is used to determine the target business that needs to be shut down based on the business operation status and the business importance when the server energy is in emergency, and shut down the target business.

6. The device according to claim 5, characterized in that The prediction unit is specifically used for: Establishing an energy consumption model based on the historical business data and the energy consumption data; The business operation status is predicted according to the energy consumption model.

7. The device according to claim 6, characterized in that The prediction unit is specifically used for: Determining a service type of the server based on the historical service data; Determine a power consumption curve of the server according to the energy consumption data; The service type and the power consumption curve are analyzed to obtain the energy consumption model.

8. The device according to claim 7, characterized in that The processing unit is also used for: The importance of the business is determined by using the business type and a pre-defined business energy consumption guarantee level table.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the emergency energy saving method as claimed in any one of claims 1 to 4.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the emergency energy saving method according to any one of claims 1 to 4 is implemented.

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