CN104391560A - Hopfield neural network-based server energy-saving method and device for cloud data center - Google Patents
Hopfield neural network-based server energy-saving method and device for cloud data center Download PDFInfo
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- CN104391560A CN104391560A CN201410507862.3A CN201410507862A CN104391560A CN 104391560 A CN104391560 A CN 104391560A CN 201410507862 A CN201410507862 A CN 201410507862A CN 104391560 A CN104391560 A CN 104391560A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3206—Monitoring of events, devices or parameters that trigger a change in power modality
- G06F1/3209—Monitoring remote activity, e.g. over telephone lines or network connections
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3206—Monitoring of events, devices or parameters that trigger a change in power modality
- G06F1/3215—Monitoring of peripheral devices
- G06F1/3225—Monitoring of peripheral devices of memory devices
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Abstract
The invention discloses a Hopfield neural network-based server energy-saving device for a cloud data center. The device comprises a data storage part, a control part and an energy-saving strategy training part, wherein the data storage part is used for storing monitoring data and energy-saving strategy information of a server group; the control part is responsible for service control, wherein the service control comprises generation and acquisition of the monitoring data, and matching and implementation of an energy-saving strategy; the energy-saving strategy training part is used for training a Hopfield neural network based on the monitoring data to generate the energy-saving strategy information. The invention also discloses a corresponding method. According to the device and the method, the problems that most energy-saving strategies are single in setting and are not accurate and reasonable enough, and the energy consumption of the data center cannot be adjusted very well are effectively solved.
Description
Technical field
The present invention relates to the administration and monitoring module in cloud computing operating system, be specifically related to cloud data center based on the server power-economizing method of Hopfield neural network and device.
Background technology
According to the definition of wikipedia, cloud computing (Cloud Computing) is a kind of account form based on internet, and in this way, the software and hardware resources shared and information can be supplied to computing machine and other equipment by demand.
Cloud computing be continue the 1980's mainframe computer to client-server big change after another great change.User no longer needs the details understanding infrastructure in " cloud ", need not have corresponding professional knowledge, also without the need to directly controlling.Cloud computing describes a kind of new IT service based on internet to be increased, uses and delivery mode, is usually directed to provide dynamically easily expansion by internet and is often virtualized resource.
Cloud computing is approved by industry gradually, and cloud data center operation system realizes gradually and is committed to practice, plays more and more important effect in social production and sphere of life.Huge based on number of devices in the large-scale cloud data center that cloud computing operating system builds, monitoring management process is complicated, and how effectively realizing the energy-efficient of cloud data center is a good problem to study.
Current most of Energy Saving Strategy only considers temperature triggered or power trigger when arranging, comprehensive analysis and consideration is not carried out to real time load information such as CPU, internal memory, the network bandwidth, the disk I/O etc. of server, strategy setting is single, there is energy-saving effect bad, the problem that entire system power consumption is higher.
Summary of the invention
In order to solve the problems of the technologies described above, the invention provides cloud data center based on the server power-economizing method of Hopfield neural network and device, for the association of cloud data center server group trains based on large-scale server load monitoring data the Hopfield neural network Energy Saving Strategy model drawn, realize the load of cloud data center to regulate and control more accurately and efficiently, improve data center's energy-saving effect and resource overall utilization rate.
Cloud data center is based on a server energy saver for Hopfield neural network, and this device comprises:
Data storage section, for monitor data and the Energy Saving Strategy information of storage server group;
Control section, for responsible Service control, described Service control comprises generation and the acquisition of monitor data, the coupling of Energy Saving Strategy and enforcement;
Energy Saving Strategy training part, for training Hopfield neural network based on described monitor data, generates Energy Saving Strategy information.
Preferably, Energy Saving Strategy training department divides and comprises Hopfield neural network Energy Saving Strategy training module, for training Hopfield neural network based on large-scale monitor data, generate Energy Saving Strategy information, wherein, the output of described Hopfield neural network model is set as three kinds of Energy Saving Strategy: high capacity Energy Saving Strategy, middle high capacity Energy Saving Strategy and in low load Energy Saving Strategy.
Preferably, data storage section comprises:
Energy Saving Strategy memory module, for storing the Energy Saving Strategy information that Hopfield neural network Energy Saving Strategy training module generates;
Supervising data storage module, for storing real-time monitoring data.
Preferably, monitor data is four-dimensional input vector P={c, m, s, n}, and wherein, c, m, s, n represent cpu load ratio, internal memory load percentage, disk I/O load ratio, network bandwidth load percentage respectively.
Preferably, control section comprises:
Entire system monitoring control module, for monitoring in real time the load of server group, reads and write operation monitor data; According to the load threshold of the server group of setting, when judging to need to implement Energy Saving Strategy, send the control information of configuration Energy Saving Strategy to Energy Saving Strategy Configuration Manager;
Energy Saving Strategy Configuration Manager, for according to described control information, current real-time monitoring data is obtained from described data memory module, as the input of the Hopfield neural network model trained, coupling generates the current Energy Saving Strategy of described server group, implement to Energy Saving Strategy the control information that module sends Energy Saving Strategy enforcement, described control information comprises current Energy Saving Strategy;
Energy Saving Strategy implements module, the control information that the Energy Saving Strategy sent for receiving Energy Saving Strategy Configuration Manager is implemented, according to current Energy Saving Strategy, concrete operation is carried out to the server apparatus in server group, to reach the energy-conservation object of cloud data center.
Cloud data center is based on a server power-economizing method for Hopfield neural network, and the method comprises:
The load of real-time monitoring server group, obtains monitor data;
According to the load threshold of the server group of setting, when judging to need to implement Energy Saving Strategy, obtain the current Energy Saving Strategy of described server group according to described monitor data coupling;
According to described current Energy Saving Strategy, power-save operation is implemented to described server group.
Preferably, the method also comprises:
Described monitor data is four-dimensional input vector P={c, m, s, n}, and wherein, c, m, s, n represent cpu load ratio, internal memory load percentage, disk I/O load ratio, network bandwidth load percentage respectively.
Preferably, described coupling according to monitor data, obtains the current Energy Saving Strategy of server group, comprising:
Using the input of the monitor data of acquisition as the Hopfield neural network model trained, coupling generates the current Energy Saving Strategy of this server group described.
Preferably, described Hopfield neural network model forms based on large-scale monitor data training; Wherein, the output of described Hopfield neural network model is set as three kinds of Energy Saving Strategy: high capacity Energy Saving Strategy, middle high capacity Energy Saving Strategy and in low load Energy Saving Strategy.
Preferably, power-save operation is implemented to described server group, comprise one or more in following operation: key business migration, server start/shutdown.
The embodiment of the present invention is by training based on large-scale server load sample data the Hopfield neural network Energy Saving Strategy model drawn for the association of cloud data center server group is arranged, nonlinear neural network model is applied to the output of data center's Energy Saving Strategy, propose for cloud data center server group energy-saving scheme accurately and efficiently, effectively improving most of Energy Saving Strategy arranges single, rationally accurate not, can not regulate the problem of consumption of data center well.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, and form a application's part, schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the server energy saver schematic diagram of embodiment of the present invention cloud data center based on Hopfield neural network;
Fig. 2 is Hopfield neural network model schematic diagram;
Fig. 3 is the server power-economizing method process flow diagram of embodiment of the present invention cloud data center based on Hopfield neural network.
Embodiment
Hopfield network is an important milestone in neural network developing history.Being taught by physicist J.J.Hopfield and propose in nineteen eighty-two, is a kind of individual layer Feedback Neural Network.
Hopfield neural network has function of associate memory, after setting up training sample, when input approximate sample can export this sample.The embodiment of the present invention has used this characteristic of Hopfield neural network just.
The embodiment of the present invention is different from other energy-conservation method parts and is: when Energy Saving Strategy is arranged, by carrying out monitoring in real time and comprehensive analysis to the various aspects of the operating real time load information of cloud data center server, utilize Hopfield neural network model to carry out the basis of training study draws corresponding Energy Saving Strategy model to the extensive monitor data sample obtained simultaneously.
Below in conjunction with drawings and the specific embodiments, the present invention is described in detail.
The basic thought of the embodiment of the present invention: the Energy Saving Strategy model drawn based on Hopfield neural metwork training, using the input of monitor data as Energy Saving Strategy model, exports and obtains corresponding Energy Saving Strategy and implement.This is a kind of Energy Saving Strategy with adaptivity, because Energy Saving Strategy model is through learning training, so the demand of the Energy Saving Strategy drawn just closing to reality more.The embodiment of the present invention provides a kind of mode more effectively and reasonably for server Energy Saving in cloud data center.
Embodiment of the present invention cloud data center based on the server energy saver of Hopfield neural network module map as shown in Figure 1, comprising:
1) Energy Saving Strategy training part
Energy Saving Strategy training part, for training Hopfield neural network based on large-scale monitor data, generates Energy Saving Strategy information.This part be online under, carry out when namely nonsystematic runs, comprising:
Hopfield neural network Energy Saving Strategy training module, for based on monitor data training study Energy Saving Strategy, generates Energy Saving Strategy information.Hopfield neural network model be input as monitor data, here, each monitor data is four-dimensional input vector P={c, m, s, n}, represents cpu load ratio (%), internal memory (memory) load percentage, disk (disk) IO load percentage, network (net) bandwidth load ratio respectively; The output of Hopfield neural network model is set as three kinds of Energy Saving Strategy: high capacity Energy Saving Strategy, middle high capacity Energy Saving Strategy and in low load Energy Saving Strategy; Hopfield neural network model is set as 3-tier architecture, is respectively input layer (4 neurons), middle layer (4 neurons) and output layer (2 neurons).Each neuron represents a vectorial dimensional information, wherein, 4 neurons of input layer are exactly each dimensional information of the four-dimensional input vector P of monitor data, the data that middle layer obtains are the data under the existing Hopfield neural computing of employing, 2 neurons of output layer can be 0 and 1 respectively, as judging the logical value adopting that Energy Saving Strategy.Energy Saving Strategy is arranged according to the demand of reality, and after to the study of a large amount of monitor datas, Hopfield neural network Energy Saving Strategy training module can generate Energy Saving Strategy information according to arranging.The concrete operations of Energy Saving Strategy comprise: key business migration, server switching on and shutting down etc.For different Energy Saving Strategy, the scope that the operation for key business migration, server switching on and shutting down is implemented is different.
2) data storage section
Data storage section is used for the real-time monitoring data of storage server group, and the Energy Saving Strategy information that aforementioned Hopfield neural network Energy Saving Strategy training module generates, and comprising:
Energy Saving Strategy memory module, for storing the Energy Saving Strategy information that Hopfield neural network Energy Saving Strategy training module generates;
Supervising data storage module, for storing real-time monitoring data;
Wherein, arrow represents data stream or the control flow check of transmission.
3) control section
Control section is responsible for Service control, and as generation and the acquisition of monitor data, the coupling generation, enforcement etc. of Energy Saving Strategy, comprising:
Entire system monitoring control module, for monitoring in real time the load of server group, reads and write operation monitor data; According to the load threshold of the server group of setting, judge whether to need to implement Energy Saving Strategy; If so, then send to Energy Saving Strategy Configuration Manager the control information implementing Energy Saving Strategy;
Energy Saving Strategy Configuration Manager, for the monitor data according to input, generates current Energy Saving Strategy based on the Hopfield neural network model coupling trained; Also for the control information according to acquisition, using the input of the monitor data obtained from monitor data module as the Hopfield neural network model trained, mate the Energy Saving Strategy in energy-conservation policy store module, obtain and this server group at this moment between the Energy Saving Strategy of Point matching; Meanwhile, implement to Energy Saving Strategy the control information that module sends Energy Saving Strategy enforcement;
Energy Saving Strategy implements module, the control information that the Energy Saving Strategy sent for receiving Energy Saving Strategy Configuration Manager is implemented, according to current Energy Saving Strategy, concrete operation is carried out to the server apparatus in server group, as key business migration, server switching on and shutting down etc., to reach the energy-conservation object of cloud data center.
Based on said apparatus, the embodiment of the present invention also proposed the server power-economizing method of a kind of cloud data center based on Hopfield neural network, as shown in Figure 3, comprising:
Step 10: train Hopfield neural network model based on large-scale monitor data, generates Energy Saving Strategy information;
The structure of the Hopfield neural network model involved by the embodiment of the present invention as shown in Figure 2, Hopfield neural network model be input as monitor data, each monitor data is four-dimensional input vector P={c, m, s, n}, represents cpu load ratio (%), internal memory (memory) load percentage, disk (disk) IO load percentage, network (net) bandwidth load ratio respectively; The output of Hopfield neural network model is set as three kinds of Energy Saving Strategy: high capacity Energy Saving Strategy, middle high capacity Energy Saving Strategy and in low load Energy Saving Strategy; Hopfield neural network model is set as 3-tier architecture, is respectively input layer (4 neurons), middle layer (4 neurons) and output layer (2 neurons).Each neuron represents a vectorial dimensional information, wherein, 4 neurons of input layer are exactly each dimensional information of the four-dimensional input vector P of monitor data, the data that middle layer obtains are the data under the existing Hopfield neural computing of employing, 2 neurons of output layer can be 0 and 1 respectively, as judging the logical value adopting that Energy Saving Strategy.The neural network model that can be used for the Energy Saving Strategy output of cloud data center is obtained through training.The concrete operations of Energy Saving Strategy comprise: key business migration, server switching on and shutting down etc.
Step 20: the load of monitoring server group in real time, obtains monitor data; According to the load threshold of the server group of setting, judge whether to need to implement Energy Saving Strategy; If so, perform step 30, otherwise continue to judge;
The entire system monitoring control module of control section writes with certain hour interval (can independently set) supervising data storage module to data storage section or reads data, the overall load at monitor data center; And according to the load threshold of server group of setting, judge whether to need to implement Energy Saving Strategy; If so, then send to the Energy Saving Strategy Configuration Manager belonging to control section together the control information implementing Energy Saving Strategy;
Step 30: according to the Energy Saving Strategy of monitor data coupling current server group, Energy Saving Strategy is implemented to server group.
The Energy Saving Strategy Configuration Manager of control section is according to the control information obtained, using the input of the monitor data obtained from monitor data module as the Hopfield neural network model trained, mate the Energy Saving Strategy in energy-conservation policy store module, obtain and this server group at this moment between the Energy Saving Strategy of Point matching; Meanwhile, implement to the Energy Saving Strategy being all control section the control information that module sends Energy Saving Strategy enforcement;
The Energy Saving Strategy of control section implements the control information of module according to the Energy Saving Strategy enforcement obtained, and the concrete Energy Saving Strategy information of association based on this server group in data storage section Energy Saving Strategy memory module, power-save operation is carried out to this server group, as operations such as key business migration, server switching on and shutting down, realize the reasonable, energy-efficient of cloud data center entirety.
The all or part of step that one of ordinary skill in the art will appreciate that in said method is carried out instruction related hardware by program and is completed, and described program can be stored in computer-readable recording medium, as ROM (read-only memory), disk or CD etc.Alternatively, all or part of step of above-described embodiment also can use one or more integrated circuit to realize.Correspondingly, each module/unit in above-described embodiment can adopt the form of hardware to realize, and the form of software function module also can be adopted to realize.The application is not restricted to the combination of the hardware and software of any particular form.
The above, be only preferred embodiments of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a Zhong Yun data center is based on the server energy saver of Hopfield neural network, it is characterized in that, this device comprises:
Data storage section, for monitor data and the Energy Saving Strategy information of storage server group;
Control section, for responsible Service control, described Service control comprises generation and the acquisition of monitor data, the coupling of Energy Saving Strategy and enforcement;
Energy Saving Strategy training part, for training Hopfield neural network based on described monitor data, generates Energy Saving Strategy information.
2. device as claimed in claim 1, is characterized in that,
Energy Saving Strategy training department divides and comprises Hopfield neural network Energy Saving Strategy training module, for training Hopfield neural network based on large-scale monitor data, generate Energy Saving Strategy information, wherein, the output of described Hopfield neural network model is set as three kinds of Energy Saving Strategy: high capacity Energy Saving Strategy, middle high capacity Energy Saving Strategy and in low load Energy Saving Strategy.
3. device as claimed in claim 1, it is characterized in that, data storage section comprises:
Energy Saving Strategy memory module, for storing the Energy Saving Strategy information that Hopfield neural network Energy Saving Strategy training module generates;
Supervising data storage module, for storing real-time monitoring data.
4. device as claimed in claim 1, is characterized in that,
Monitor data is four-dimensional input vector P={c, m, s, n}, and wherein, c, m, s, n represent cpu load ratio, internal memory load percentage, disk I/O load ratio, network bandwidth load percentage respectively.
5. device as claimed in claim 1 or 2 or 3 or 4, it is characterized in that, control section comprises:
Entire system monitoring control module, for monitoring in real time the load of server group, reads and write operation monitor data; According to the load threshold of the server group of setting, when judging to need to implement Energy Saving Strategy, send the control information of configuration Energy Saving Strategy to Energy Saving Strategy Configuration Manager;
Energy Saving Strategy Configuration Manager, for according to described control information, current real-time monitoring data is obtained from described data memory module, as the input of the Hopfield neural network model trained, coupling generates the current Energy Saving Strategy of described server group, implement to Energy Saving Strategy the control information that module sends Energy Saving Strategy enforcement, described control information comprises current Energy Saving Strategy;
Energy Saving Strategy implements module, the control information that the Energy Saving Strategy sent for receiving Energy Saving Strategy Configuration Manager is implemented, according to current Energy Saving Strategy, concrete operation is carried out to the server apparatus in server group, to reach the energy-conservation object of cloud data center.
6. a Zhong Yun data center is based on the server power-economizing method of Hopfield neural network, and it is characterized in that, the method comprises:
The load of real-time monitoring server group, obtains monitor data;
According to the load threshold of the server group of setting, when judging to need to implement Energy Saving Strategy, obtain the current Energy Saving Strategy of described server group according to described monitor data coupling;
According to described current Energy Saving Strategy, power-save operation is implemented to described server group.
7. method as claimed in claim 6, it is characterized in that, the method also comprises:
Described monitor data is four-dimensional input vector P={c, m, s, n}, and wherein, c, m, s, n represent cpu load ratio, internal memory load percentage, disk I/O load ratio, network bandwidth load percentage respectively.
8. method as claimed in claims 6 or 7, is characterized in that,
Described coupling according to monitor data, obtains the current Energy Saving Strategy of server group, comprising:
Using the input of the monitor data of acquisition as the Hopfield neural network model trained, coupling generates the current Energy Saving Strategy of this server group described.
9. method as claimed in claim 8, is characterized in that,
Described Hopfield neural network model forms based on large-scale monitor data training; Wherein, the output of described Hopfield neural network model is set as three kinds of Energy Saving Strategy: high capacity Energy Saving Strategy, middle high capacity Energy Saving Strategy and in low load Energy Saving Strategy.
10. method as claimed in claims 6 or 7, is characterized in that,
Power-save operation is implemented to described server group, comprises one or more in following operation: key business migration, server start/shutdown.
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