CN117148955B - Data center energy consumption management method based on energy consumption data - Google Patents
Data center energy consumption management method based on energy consumption data Download PDFInfo
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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
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/3287—Power saving characterised by the action undertaken by switching off individual functional units in the computer system
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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
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3024—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a central processing unit [CPU]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
- G06F11/3062—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations where the monitored property is the power consumption
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H03M7/3084—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction using adaptive string matching, e.g. the Lempel-Ziv method
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a data center energy consumption management method based on energy consumption data, relates to the field of energy management, and solves the problems that the energy consumption management of the traditional data center is difficult to properly optimize according to the running states of different server nodes, so that the energy waste and the low energy utilization efficiency are caused, and the method comprises the following steps: step S1: acquiring basic data of a data center, and step S2: performing exception processing according to basic data of a data center, and step S3: performing data processing according to the basic data and the exception handling data of the data center, and step S4: according to the method, the energy consumption ratio of the server node of the data center is calculated, and different energy consumption management modes are respectively formulated for the service node with high energy consumption ratio and the service node with low energy consumption ratio according to the calculation result, so that the pertinence and the applicability of the energy consumption management method of the data center are improved.
Description
Technical Field
The invention belongs to the field of energy management, relates to an energy consumption optimization technology, and particularly relates to a data center energy consumption management method based on energy consumption data.
Background
The data center is a facility specially used for storing, processing, managing and transmitting a large amount of data, and is generally composed of a large amount of servers, network equipment, storage equipment and cooling equipment, and is mainly used for hosting and running computing tasks such as application servers, big data analysis and the like of various enterprises.
The traditional data center energy consumption management mainly focuses on unified planning and optimization, is difficult to properly optimize according to the running states of different server nodes, lacks the capability of dynamically adjusting energy configuration in real time, causes the data center to lack pertinence in energy consumption management, is difficult to efficiently adjust energy use and configuration, and causes energy waste and energy utilization efficiency reduction.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a data center energy consumption management method based on energy consumption data.
The invention aims at realizing the following technical scheme, and the data center energy consumption management method based on the energy consumption data comprises the following steps:
step S1: acquiring basic data of a data center;
step S2: performing exception processing according to basic data of a data center;
step S3: performing data processing according to the basic data and the exception handling data of the data center;
step S4: and performing energy consumption management on the server node.
Further, in the step S1, basic data of the data center is obtained, and the specific steps are as follows:
step S11: acquiring energy consumption data of a server node;
step S12: load data of a server node is obtained;
step S13: acquiring a temperature value of an environment where the server node is located through a temperature sensor, acquiring a humidity value of the environment where the server node is located through a humidity sensor, and setting the temperature value and the humidity value of the environment where the server node is located as environment data;
step S14: acquiring hardware use data, message queues, log records, monitoring data and other data transmitted by the server node mutually, and setting the data as network data;
step S15: and setting the energy consumption data of the server node, the load data of the server node, the environment data and the network data as basic data of a data center.
Further, in the step S11, the energy consumption data of the server node is obtained, which specifically includes the following steps:
step S111: q time nodes are obtained through a time counter, current data of the q time nodes through a server node are obtained through a current sensor, voltage data of the q time nodes are obtained through a voltage sensor, and a power factor of the server node is obtained through a power quality analyzer;
step S112: calculating to obtain an average current value of the server node during working by using the current data of the q time nodes through the server node and the power factor of the server node;
step S113: calculating to obtain an average voltage value of the server node during working by using the q time nodes through the voltage data of the server node and the power factor of the server node;
step S114: and calculating to obtain the energy consumption data of the server node according to the average current value of the server node during operation, the average voltage value of the server node during operation, the operation time length data of the server node and the power factor of the server node.
Further, in the step S12, load data of the server node is obtained, which specifically includes the following steps:
Step S121: dividing the working time length data into m identical time units according to seconds, and respectively acquiring CPU utilization rate, memory utilization rate and network flow of server nodes corresponding to the m identical time units through a task manager;
step S122: calculating to obtain the average CPU utilization rate of the server node according to the CPU utilization rates of the server nodes corresponding to the m same time units, calculating to obtain the average memory utilization rate of the server node according to the memory utilization rates of the server nodes corresponding to the m same time units, and calculating to obtain the average network flow of the server node according to the network flow of the server nodes corresponding to the m same time units;
step S123: and calculating to obtain load data of the server node according to the average CPU utilization rate of the server node, the average memory utilization rate of the server node and the average network flow of the server node.
Further, in the step S2, exception processing is performed according to the basic data of the data center, which specifically includes the following steps:
step S21: the energy consumption ratio of the server node is calculated, and the specific steps are as follows:
step S211: respectively acquiring a working environment temperature value and a humidity value recommended by a server manufacturer document according to the server manufacturer document, and respectively acquiring energy consumption data of a server node, load data of the server node and a temperature value and a humidity value of an environment where the server node is located according to basic data of a data center;
Step S212: calculating to obtain energy consumption ratio data Nb of the server node according to load data, energy consumption data, temperature values and humidity values of an environment where the server node is located and working environment temperature values and humidity values recommended by a document of a server manufacturer;
step S22: performing abnormality judgment according to the energy consumption ratio data;
step S23: and respectively transmitting the server node corresponding to the normal state and the server node corresponding to the abnormal state to the data processing module as abnormal processing data.
Further, in the step S22, the abnormality determination is performed according to the energy consumption ratio data, and the specific steps are as follows:
step S221: acquiring full load data of a server node and rated power consumption data of the server according to a server manufacturer document, and acquiring minimum data of the server node and reference power consumption data of the server according to the server manufacturer document;
step S222: calculating to obtain energy consumption ratio threshold value data Nb1 of the server node according to full load data of the server node, reference power consumption data of the server node, temperature values of environments where the server node is located, humidity values of the environments where the server node is located, working environment temperature values recommended by a server manufacturer document and working environment humidity values recommended by the server manufacturer document;
Step S223: calculating to obtain energy consumption ratio threshold value data Nb2 of the server node according to the empty load data of the server node, rated power consumption data of the server node, temperature value of the environment where the server node is located, humidity value of the environment where the server node is located, working environment temperature value recommended by a server manufacturer document and working environment humidity value recommended by the server manufacturer document;
step S224: obtaining abnormal processing data according to the energy consumption ratio threshold value data of the server node and the energy consumption ratio data of the server node:
when Nb2 is more than 0 and less than Nb1, the server node is in a normal state;
when Nb is more than or equal to Nb1 and more than 0 or less than Nb is more than or equal to Nb2, the server node is in an abnormal state.
Further, in the step S3, data processing is performed according to the basic data and the exception handling data of the data center, and the specific steps are as follows:
step S31: the energy consumption ratio grading is carried out on the server nodes in the normal state, and the specific steps are as follows:
step S311: acquiring server node energy consumption ratio data in a normal state through exception processing data;
step S312: randomly selecting u server nodes of a data center, acquiring the energy consumption ratio of the u servers, calculating the average energy consumption ratio of the u server nodes through the energy consumption ratio acquisition of the u servers, and setting the average energy consumption ratio as server node energy consumption ratio threshold data;
Step S313: and judging the energy consumption ratio of the server node in a normal state, wherein the energy consumption ratio is specifically as follows:
when Nb is more than or equal to Nbp, judging that the node is a service node with high energy consumption ratio;
when Nbp is more than Nb and more than or equal to 0, judging that the node is a service node with low energy consumption ratio;
step S314: setting the judgment result as server node energy consumption ratio grading data;
step S32: and compressing the network data.
Further, in the step S32, the compression processing is performed on the network data, which specifically includes the following steps:
step S321: the transmission node of the network data comprises a server node of a sending end and a server node of a receiving end, when the server node of the sending end sends an HTTP request to the server node of the receiving end, an Accept-Encoding field is added in a request header, and after receiving the request, the server node of the receiving end judges whether the Gzip compression is supported or not;
step S322: if so, adding a Content-Encoding field in the response header, and transmitting the network data compressed by the Gzip to a server node of the receiving end by a server node of the transmitting end;
step S3221: using a character string matching algorithm to acquire repeated character strings in the network data, replacing the repeated character strings in the network data by character strings based on Lempel-Ziv-Welch (LZW), and replacing the repeated character strings with short pointers and length codes so as to reduce the byte number of the repeated character strings;
Step S3221: compression encoding is carried out on the replaced short pointer and the replaced length code by using Huffman coding, so as to complete Gzip compression of network data;
step S323: if not, the server node of the transmitting end transmits the uncompressed network data to the server node of the receiving end.
Further, in the step S4, energy consumption management of the server node is completed, and the specific steps are as follows:
step S41: the abnormal server node is subjected to abnormal management, and the specific steps are as follows:
step S411: the method comprises the steps of receiving exception handling data, obtaining a work log of a server node in an exception state, and determining the exception reason of the server node according to the work log, wherein the exception reason of the server node comprises hardware faults and software faults;
step S412: aiming at hardware faults, server node maintenance personnel are arranged to overhaul and replace corresponding hardware, so that the server node returns to a normal working state;
step S413: aiming at software faults, updating a software system of the server node, repairing a configuration file and reinstalling components to enable the server node to recover to a normal working state;
step S42: and performing energy consumption management on the server node.
Further, in the step S42, the energy consumption management performed by the server node is completed, and the specific steps are as follows:
Step S421: obtaining energy consumption ratio grading data of a server node;
step S422: aiming at the high energy consumption ratio service node, the energy consumption management mode is specifically as follows:
step S4221: the method comprises the steps that configuration information and load states of a server node are obtained through a task manager, and a virtual machine is utilized to clone a configuration file and a network file of the current server node;
step S4222: selecting a cloud server according to the load condition of the server node, creating a cloud server which is the same as the configuration information of the server node in the cloud server, and applying the configuration of the server node to the mirror image of the cloud server through the configuration information;
step S4223: transmitting network data of the server node to a cloud server through file transmission service of the cloud server for cloud storage, importing a mirror image into the cloud server, setting a network and configuring a security policy;
step S4224: running a test workload on a cloud server, verifying the performance and stability of the migrated system, and ensuring the normal running and response of the migrated virtual machine and application program;
step S423: aiming at the low energy consumption ratio service ratio node, the energy consumption management mode is specifically as follows:
step S4231: the DCIM is used for monitoring the server node with low energy consumption ratio in real time, and identifying the energy consumption peak and the energy consumption valley of the server node;
Step S42311: aiming at energy consumption peaks, the management strategy for the server node is specifically as follows:
step S423111: the server nodes distribute the requests to different server nodes through a load balancing strategy, so that overload of a certain node is avoided, and the overall operation efficiency is improved;
step S423112: a dynamic power consumption adjustment strategy is adopted for the server node, computing resources are provided according to actual requirements and load conditions, and the processor frequency of a server node assembly is reduced;
step S42312: aiming at the low energy consumption, the management strategy for the server node is specifically as follows:
step S423121: CPU and memory resources of the server node are reduced, resources are allocated according to the needs, and energy consumption of the server node is reduced;
step S423121: and increasing the load monitoring frequency of the server nodes, merging tasks of the server nodes with the working frequency of the processor, and closing part of the server nodes.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. according to the invention, the server node is subjected to abnormality judgment and abnormality processing in time, so that the waste of energy sources by the server node in an abnormal working state of the data center is reduced, and the energy consumption management of the data center is realized.
2. According to the method, the energy consumption ratio of the server node of the data center is calculated, and different energy consumption management modes are respectively formulated for the service node with high energy consumption ratio and the service node with low energy consumption ratio according to the calculation result, so that the pertinence and the applicability of the energy consumption management method of the data center are greatly improved.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a diagram of the steps in the practice of the present invention;
FIG. 2 is a system frame diagram of the present invention;
fig. 3 is a schematic diagram of a cloud server according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1 and 2, the present invention provides a technical solution: the energy consumption management system of the data center based on the energy consumption data comprises a data acquisition module, an abnormality management module, a data processing module and an energy consumption management module, wherein the data acquisition module, the abnormality management module, the data processing module and the energy consumption management module are respectively connected with a server;
The data acquisition module acquires basic data of a data center;
the data acquisition module comprises an energy consumption data unit, a load data unit, an environment data unit and a network data unit, wherein the energy consumption data unit acquires energy consumption data of a server node, the load data unit acquires load data of the server node, the environment data unit acquires environment data of the server node, the network data unit acquires network data of the server node, the energy consumption data unit comprises a time counter, a current sensor, a voltage sensor and a power quality analyzer, the load data unit comprises a task manager built in the server node, and the environment data unit comprises a temperature sensor and a humidity sensor;
the energy consumption data unit acquires energy consumption data of a server node, and specifically comprises the following steps:
acquiring working time length data of a server node through a time counter, transmitting the working time length data to a load data unit, dividing the working time length data into q time nodes, wherein q is a positive integer larger than 0 and is j1, j2 and j3 … … jq respectively, acquiring current data of the q time nodes through the server node through a current sensor and is Ij1, ij2 and Ij3 … … Ijq respectively, acquiring voltage data of the q time nodes through the server node through a voltage sensor and is Vj1, vj2 and Vj3 … … Vjq respectively, and acquiring a power factor of the server node through a power quality analyzer;
According to the formulaCalculating to obtain an average current value of the server node during operation;
according to the formulaCalculating an average voltage value of the nodes with the servers in operation;
according to the formulaCalculating to obtain energy consumption data Pg of the server node;
it can be understood that:
pg is energy consumption data of a server node, ip is an average current value when the server node works, vp is an average voltage value when the server node works, tg is working time length data of the server node, wy is a power factor of the server node, a1 is a set power factor proportional coefficient, and a1 is larger than 0;
what needs to be explained here is:
the power factor is an index for measuring the relation between useful power and apparent power in an alternating current circuit, and describes the phase difference between the power for actually executing useful work in the circuit and input or output current, and the value range of the power factor is between 0 and 1, wherein 1 represents the useful power fully utilized in the circuit;
the load data unit acquires load data of the server node, and the load data unit comprises the following specific steps:
dividing the working time length data into m identical time units according to seconds, namely s1, s2 and s3 … … sm respectively, acquiring CPU utilization rates of m identical time unit corresponding server nodes respectively as Cs1, cs2 and Cs3 … … Csm through a task manager, acquiring memory utilization rates of m identical time unit corresponding server nodes respectively as Ns1, ns2 and Ns3 … … Nsm through the task manager, and acquiring network flows of m identical time unit corresponding server nodes respectively as LS1, LS2 and LS3 … … Lsm through the task manager;
According to the formulaCalculating to obtain the average CPU utilization Cp of the server node;
according to the formulaCalculating to obtain the average memory utilization rate Np of the server node;
according to the formulaCalculating to obtain the average network flow Lp of the server node;
according to the formulaCalculating to obtain load data of the server node;
it can be understood that:
fz is load data of the server node, cp is average CPU utilization rate Cp of the server node, np is average memory utilization rate of the server node, lp is average network flow of the server node, tg is working time length data of the server node, b1 and b2 are set proportionality coefficients, and b1 and b2 are both greater than 0;
the environment data unit acquires environment data of the server node, and the environment data unit comprises the following specific steps:
acquiring a temperature value of an environment where the server node is located through a temperature sensor, and acquiring a humidity value of the environment where the server node is located through a humidity sensor;
setting a temperature value and a humidity value of an environment where the server node is located as environment data;
the network data unit acquires the data transmitted by the hardware use data, the message queue, the log record, the monitoring data and the like of the server node and sets the data as network data;
The data acquisition module is used for setting the energy consumption data of the server node, the load data of the server node, the environment data and the network data as basic data of a data center and transmitting the basic data to the abnormality management module and the data processing module;
the abnormality management module performs abnormality processing according to the basic data of the data center;
the abnormality management module comprises an energy efficiency ratio unit and an abnormality judgment unit, wherein the energy efficiency ratio unit calculates the energy consumption ratio of the server node, and the abnormality judgment unit judges the abnormality of the server node, and the energy efficiency ratio unit and the abnormality judgment unit comprise server manufacturer documents;
the energy efficiency ratio unit calculates the energy consumption ratio of the server node, and specifically comprises the following steps:
respectively acquiring a working environment temperature value recommended by a server manufacturer document and a working environment humidity value recommended by the server manufacturer document according to the server manufacturer document;
respectively acquiring energy consumption data of a server node, load data of the server node, a temperature value of an environment where the server node is located and a humidity value of the environment where the server node is located according to basic data of a data center;
according to the formulaCalculating to obtain energy consumption ratio data Nb of the server node, wherein Nb is larger than 0;
It can be understood that:
nb is energy consumption ratio data of a server node, fz is load data of the server node, pg is energy consumption data of the server node, wd is a temperature value of an environment where the server node is located, sd is a humidity value of the environment where the server node is located, bw is a working environment temperature value recommended by a server manufacturer document, bs is a working environment humidity value recommended by the server manufacturer document, c1 and c2 are set proportionality coefficients, and both c1 and c2 are larger than 0;
the abnormality judgment unit performs abnormality judgment according to the energy consumption ratio data, specifically as follows:
acquiring full load data of a server node and rated power consumption data of the server according to a server manufacturer document, and acquiring minimum data of the server node and reference power consumption data of the server according to the server manufacturer document;
according to the formulaCalculating to obtain energy consumption ratio threshold data Nb1 of the server node, wherein Nb1 is larger than 0;
it can be understood that: nb1 is threshold value data of energy consumption ratio of a server node, fz1 is full load data of the server node, pg1 is reference power consumption data of the server node, wd is temperature value of the environment where the server node is located, sd is humidity value of the environment where the server node is located, bw is working environment temperature value recommended by a server manufacturer document, bs is working environment humidity value recommended by the server manufacturer document, c1 and c2 are set proportionality coefficients, and both c1 and c2 are larger than 0;
According to the formulaCalculating to obtain energy consumption ratio threshold data Nb2 of the server node, wherein Nb2 is larger than 0;
it can be understood that:
nb2 is energy consumption ratio threshold data of a server node, fz2 is empty load data of the server node, pg2 is rated power consumption data of the server node, wd is a temperature value of an environment where the server node is located, sd is a humidity value of the environment where the server node is located, bw is a working environment temperature value recommended by a server manufacturer document, bs is a working environment temperature value recommended by the server manufacturer document, c1 and c2 are set proportionality coefficients, and c1 and c2 are both larger than 0;
what needs to be explained here is:
in this embodiment, the threshold data is calculated so that the temperature value and the humidity value of the environment where the server node is located are respectively the same as the working environment temperature value and the working environment humidity value recommended by the server manufacturer document;
when Nb2 is more than 0 and less than Nb1, the server node is in a normal state;
when Nb is more than or equal to Nb1 and more than 0 or less than Nb2, the server node is in an abnormal state;
the abnormal management module respectively transmits the server node corresponding to the normal state and the server node corresponding to the abnormal state as abnormal processing data to the data processing module;
The data processing module performs data processing according to the basic data and the exception handling data of the data center;
the data processing module comprises a grading processing unit and a data processing unit, the grading processing unit grades the energy consumption ratio of the server node in a normal state, and the data processing unit processes the network data;
the grading processing unit performs energy consumption ratio grading on the server nodes in a normal state, and specifically comprises the following steps:
acquiring server node energy consumption ratio data in a normal state through exception processing data;
randomly selecting u server nodes of a data center, acquiring energy consumption ratios of the u servers as Nh1, nh2 and Nh3 … … Nhu respectively through an energy efficiency ratio calculation unit, and obtaining the energy consumption ratios of the u servers through formulasCalculating to obtain the average energy consumption ratio of the u server nodes and setting the average energy consumption ratio as the threshold value data of the server nodes;
and judging the energy consumption ratio of the server node in a normal state, wherein the energy consumption ratio is specifically as follows:
when Nb is more than or equal to Nbp, judging that the node is a service node with high energy consumption ratio;
when Nbp is more than Nb and more than or equal to 0, judging that the node is a service node with low energy consumption ratio;
setting the judgment result as server node energy consumption ratio grading data and transmitting the server node energy consumption ratio grading data to an energy consumption management module;
the data processing unit processes the network data, and specifically comprises the following steps:
The transmission nodes of the network data comprise a server node of a sending end and a server node of a receiving end;
when a server node of a transmitting end transmits an HTTP request to a server node of a receiving end, adding an Accept-Encoding field in a request header, and after receiving the request, the server node of the receiving end judges whether the Gzip compression is supported or not;
if so, adding a Content-Encoding field in the response header, and transmitting the network data compressed by the Gzip to a server node of the receiving end by a server node of the transmitting end;
if not, the server node of the transmitting end transmits uncompressed network data to the server node of the receiving end;
the network data is Gzip compressed, concretely as follows:
using a character string matching algorithm to acquire repeated character strings in the network data, replacing the repeated character strings in the network data by character strings based on Lempel-Ziv-Welch (LZW), and replacing the repeated character strings with short pointers and length codes so as to reduce the byte number of the repeated character strings;
compression encoding is carried out on the replaced short pointer and the replaced length code by using Huffman coding, so as to complete Gzip compression of network data;
what needs to be explained here is:
The compression method used in the embodiment is an HTTP Gzip compression algorithm, and the size of transmission data can be obviously reduced by HTTP Gzip compression, so that occupation of network bandwidth is reduced, and energy consumption of a server node is reduced;
the energy consumption management module is used for managing energy consumption of the server node;
the energy consumption management module comprises an abnormality processing unit and an energy consumption management unit, wherein the abnormality management unit performs abnormality management, and the energy consumption management unit performs energy consumption management on the server node, and the energy consumption management unit comprises a DCIM and a load balancing strategy;
the abnormality management unit performs abnormality management, specifically as follows:
the method comprises the steps of receiving exception handling data, obtaining a work log of a server node in an exception state, and determining the exception reason of the server node according to the work log, wherein the exception reason of the server node comprises hardware faults and software faults;
aiming at hardware faults, server node maintenance personnel are arranged to overhaul and replace corresponding hardware, so that the server node returns to a normal working state;
aiming at software faults, updating, repairing configuration files and reinstalling components of a software system of the server node to enable the server node to return to a normal working state;
The energy consumption management unit manages the energy consumption of the server node, and specifically comprises the following steps:
obtaining energy consumption ratio grading data of a server node;
aiming at the high energy consumption ratio service node, the energy consumption management mode is specifically as follows:
the method comprises the steps that configuration information and load states of a server node are obtained through a task manager, and a virtual machine is utilized to clone a configuration file and a network file of the current server node;
referring to fig. 3, a cloud server is selected according to a load condition of a server node, a cloud server with the same configuration information as the server node is created in the cloud server, and the configuration of the server node is applied to a mirror image of the cloud server through the configuration information;
transmitting network data of the server node to a cloud server through file transmission service of the cloud server for cloud storage, importing a mirror image into the cloud server, setting a network and configuring a security policy;
it should be noted that: the imported image is a clone of the server node, and the original service node comprises network settings and corresponding security policies.
Running a test workload on a cloud server, verifying the performance and stability of the migrated system, and ensuring the normal running and response of the migrated virtual machine and application program;
Aiming at the low energy consumption ratio service ratio node, the energy consumption management mode is specifically as follows:
the DCIM is used for monitoring the server node with low energy consumption ratio in real time, and identifying the energy consumption peak and the energy consumption valley of the server node;
aiming at energy consumption peaks, the management strategy for the server node is specifically as follows:
(1) The server nodes distribute the requests to different server nodes through a load balancing strategy, so that overload of a certain node is avoided, and the overall operation efficiency is improved;
(2) A dynamic power consumption adjustment strategy is adopted for the server node, computing resources are provided according to actual requirements and load conditions, and the processor frequency of a server node assembly is reduced;
aiming at the low energy consumption, the management strategy for the server node is specifically as follows:
(1) CPU and memory resources of the server node are reduced, resources are allocated according to the needs, and energy consumption of the server node is reduced;
(2) And increasing the load monitoring frequency of the server nodes, merging tasks of the server nodes with the working frequency of the processor, and closing part of the server nodes.
It should be noted that:
DCIM is a set of tools and methods for monitoring, managing and optimizing data centers, and aims to improve the energy efficiency, operation efficiency and reliability of the data centers;
The load balancing policy is used to effectively distribute and manage loads (i.e., requests and traffic) to ensure load balancing among the various server nodes, improving system performance, availability and scalability.
In the present application, if a corresponding calculation formula appears, the above calculation formulas are all dimensionality-removed and numerical calculation, and the size of the weight coefficient, the scale coefficient and other coefficients existing in the formulas is a result value obtained by quantizing each parameter, so long as the proportional relation between the parameter and the result value is not affected.
Example two
Based on another conception of the same invention, a data center energy consumption management method based on energy consumption data is provided, which comprises the following steps:
step S1: acquiring basic data of a data center;
step S11: the method comprises the following specific steps of:
step S111: q time nodes are obtained through a time counter, current data of the q time nodes through a server node are obtained through a current sensor, voltage data of the q time nodes are obtained through a voltage sensor, and a power factor of the server node is obtained through a power quality analyzer;
Step S112: calculating to obtain an average current value of the server node during working by using the current data of the q time nodes through the server node and the power factor of the server node;
step S113: calculating to obtain an average voltage value of the server node during working by using the q time nodes through the voltage data of the server node and the power factor of the server node;
step S112: calculating to obtain energy consumption data of the server node according to the average current value of the server node during operation, the average voltage value of the server node during operation, the operation time length data of the server node and the power factor of the server node;
step S12: the method comprises the following specific steps of:
step S121: dividing the working time length data into m identical time units according to seconds, respectively acquiring CPU utilization rates of the server nodes corresponding to the m identical time units through a task manager, respectively acquiring memory utilization rates of the server nodes corresponding to the m identical time units through the task manager, and respectively acquiring network flows of the server nodes corresponding to the m identical time units through the task manager;
step S122: calculating to obtain the average CPU utilization rate of the server node according to the CPU utilization rates of the server nodes corresponding to the m same time units, calculating to obtain the average memory utilization rate of the server node according to the memory utilization rates of the server nodes corresponding to the m same time units, and calculating to obtain the average network flow of the server node according to the network flow of the server nodes corresponding to the m same time units;
Step S123: calculating to obtain load data of the server node according to the average CPU utilization rate of the server node, the average memory utilization rate of the server node and the average network flow of the server node;
step S13: acquiring a temperature value of an environment where the server node is located through a temperature sensor, acquiring a humidity value of the environment where the server node is located through a humidity sensor, and setting the temperature value and the humidity value of the environment where the server node is located as environment data;
step S14: acquiring data transmitted by hardware use data, message queues, log records, monitoring data and the like of the server node mutually and setting the data as network data;
step S15: setting the energy consumption data of the server node, the load data of the server node, the environment data and the network data as basic data of a data center;
step S2: performing exception processing according to basic data of a data center;
step S21: the energy consumption ratio of the server node is calculated, and the specific steps are as follows:
step S211: respectively acquiring a working environment temperature value recommended by a server manufacturer document and a working environment humidity value recommended by the server manufacturer document according to the server manufacturer document, and respectively acquiring energy consumption data of a server node, load data of the server node, a temperature value of an environment where the server node is located and a humidity value of the environment where the server node is located according to basic data of a data center;
Step S212: calculating to obtain energy consumption ratio data Nb of the server node according to load data of the server node, energy consumption data of the server node, temperature values of an environment where the server node is located, humidity values of the environment where the server node is located, working environment temperature values recommended by a server manufacturer document and working environment humidity values recommended by the server manufacturer document;
step S22: the abnormality judgment is carried out according to the energy consumption ratio data, and the specific steps are as follows:
step S221: acquiring full load data of a server node and rated power consumption data of the server according to a server manufacturer document, and acquiring minimum data of the server node and reference power consumption data of the server according to the server manufacturer document;
step S222: calculating to obtain energy consumption ratio threshold value data Nb1 of the server node according to full load data of the server node, reference power consumption data of the server node, temperature values of environments where the server node is located, humidity values of the environments where the server node is located, working environment temperature values recommended by a server manufacturer document and working environment humidity values recommended by the server manufacturer document;
step S223: calculating to obtain energy consumption ratio threshold value data Nb2 of the server node according to the empty load data of the server node, rated power consumption data of the server node, temperature value of the environment where the server node is located, humidity value of the environment where the server node is located, working environment temperature value recommended by a server manufacturer document and working environment humidity value recommended by the server manufacturer document;
Step S224: obtaining abnormal processing data according to the energy consumption ratio threshold value data of the server node and the energy consumption ratio data of the server node:
when Nb2 is more than 0 and less than Nb1, the server node is in a normal state;
when Nb is more than or equal to Nb1 and more than 0 or less than Nb2, the server node is in an abnormal state;
step S23: respectively transmitting the server nodes corresponding to the normal state and the server nodes corresponding to the abnormal state to a data processing module as abnormal processing data;
step S3: performing data processing according to the basic data and the exception handling data of the data center;
step S31: the energy consumption ratio grading is carried out on the server nodes in the normal state, and the specific steps are as follows:
step S311: acquiring server node energy consumption ratio data in a normal state through exception processing data;
step S312: randomly selecting u server nodes of a data center, acquiring the energy consumption ratio of the u servers, calculating the average energy consumption ratio of the u server nodes through the energy consumption ratio of the u servers, and setting the average energy consumption ratio as server node energy consumption ratio threshold data;
step S313: and judging the energy consumption ratio of the server node in a normal state, wherein the energy consumption ratio is specifically as follows:
when Nb is more than or equal to Nbp, judging that the node is a service node with high energy consumption ratio;
When Nbp is more than Nb and more than or equal to 0, judging that the node is a service node with low energy consumption ratio;
step S314: setting the judgment result as server node energy consumption ratio grading data;
step S32: the method comprises the following specific steps of:
step S321: the transmission node of the network data comprises a server node of a sending end and a server node of a receiving end, when the server node of the sending end sends an HTTP request to the server node of the receiving end, an Accept-Encoding field is added in a request header, and after receiving the request, the server node of the receiving end judges whether the Gzip compression is supported or not;
step S322: if so, adding a Content-Encoding field in the response header, and transmitting the network data compressed by the Gzip to a server node of the receiving end by a server node of the transmitting end;
step S3221: using a character string matching algorithm to acquire repeated character strings in the network data, replacing the repeated character strings in the network data by character strings based on Lempel-Ziv-Welch (LZW), and replacing the repeated character strings with short pointers and length codes so as to reduce the byte number of the repeated character strings;
step S3221: compression encoding is carried out on the replaced short pointer and the replaced length code by using Huffman coding, so as to complete Gzip compression of network data;
Step S323: if not, the server node of the transmitting end transmits uncompressed network data to the server node of the receiving end;
step S4: performing energy consumption management on the server node;
step S41: the abnormal server node is subjected to abnormal management, and the specific steps are as follows:
step S411: the method comprises the steps of receiving exception handling data, obtaining a work log of a server node in an exception state, and determining the exception reason of the server node according to the work log, wherein the exception reason of the server node comprises hardware faults and software faults;
step S412: aiming at hardware faults, server node maintenance personnel are arranged to overhaul and replace corresponding hardware, so that the server node returns to a normal working state;
step S413: aiming at software faults, updating, repairing configuration files and reinstalling components of a software system of the server node to enable the server node to return to a normal working state;
step S42: the energy consumption management is carried out on the server node, and the specific steps are as follows:
step S421: obtaining energy consumption ratio grading data of a server node;
step S422: aiming at the high energy consumption ratio service node, the energy consumption management mode is specifically as follows:
step S4221: the method comprises the steps that configuration information and load states of a server node are obtained through a task manager, and a virtual machine is utilized to clone a configuration file and a network file of the current server node;
Step S4222: selecting a cloud server according to the load condition of the server node, creating a cloud server which is the same as the configuration information of the server node in the cloud server, and applying the configuration of the server node to the mirror image of the cloud server through the configuration information;
step S4223: transmitting network data of the server node to a cloud server through file transmission service of the cloud server for cloud storage, importing a mirror image into the cloud server, setting a network and configuring a security policy;
step S4224: running a test workload on a cloud server, verifying the performance and stability of the migrated system, and ensuring the normal running and response of the migrated virtual machine and application program;
step S423: aiming at the low energy consumption ratio service ratio node, the energy consumption management mode is specifically as follows:
step S4231: the DCIM is used for monitoring the server node with low energy consumption ratio in real time, and identifying the energy consumption peak and the energy consumption valley of the server node;
step S42311: aiming at energy consumption peaks, the management strategy for the server node is specifically as follows:
step S423111: the server nodes distribute the requests to different server nodes through a load balancing strategy, so that overload of a certain node is avoided, and the overall operation efficiency is improved;
Step S423112: a dynamic power consumption adjustment strategy is adopted for the server node, computing resources are provided according to actual requirements and load conditions, and the processor frequency of a server node assembly is reduced;
step S42312: aiming at the low energy consumption, the management strategy for the server node is specifically as follows:
step S423121: CPU and memory resources of the server node are reduced, resources are allocated according to the needs, and energy consumption of the server node is reduced;
step S423121: and increasing the load monitoring frequency of the server nodes, merging tasks of the server nodes with the working frequency of the processor, and closing part of the server nodes.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. A data center energy consumption management method based on energy consumption data, comprising:
step S1: obtaining energy consumption data of the server node through calculation of an average current value when the server node works, an average voltage value when the server node works, working time length data of the server node and a power factor of the server node, obtaining load data of the server node through calculation of an average CPU (central processing unit) utilization rate of the server node, an average memory utilization rate of the server node and an average network flow of the server node, obtaining a temperature value and a humidity value of an environment where the server node is located as environment data, obtaining network data of the server node, and obtaining basic data of a data center by integrating the energy consumption data of the server node, the load data of the server node, the environment data and the network data;
step S2: calculating energy consumption data of the server node, load data of the server node and environment data to obtain energy consumption ratio data of the server node, and judging a threshold value set for the energy consumption ratio data of the server node to obtain exception handling data;
step S3: threshold value judgment is carried out on the server node energy consumption ratio data in a normal state through the server node energy consumption ratio threshold value data, so that server node energy consumption ratio grading data are obtained, network data are processed, and Gzip compression of the network data is realized;
Step S4: performing exception management on the server node in an exception state according to the exception handling data, and performing energy consumption management on the server node according to the energy consumption comparison grade data of the server node;
in the step S2, exception processing is performed according to the basic data of the data center, which specifically includes the following steps:
step S21: the energy consumption ratio of the server node is calculated, and the specific steps are as follows:
step S211: respectively acquiring a working environment temperature value and a humidity value recommended by a server manufacturer document according to the server manufacturer document, and respectively acquiring energy consumption data of a server node, load data of the server node and a temperature value and a humidity value of an environment where the server node is located according to basic data of a data center;
step S212: calculating to obtain energy consumption ratio data Nb of the server node according to load data, energy consumption data, temperature values and humidity values of an environment where the server node is located and working environment temperature values and humidity values recommended by a document of a server manufacturer;
according to the formulaCalculating to obtain energy consumption ratio data Nb of the server node, wherein Nb is larger than 0;
nb is energy consumption ratio data of a server node, fz is load data of the server node, pg is energy consumption data of the server node, wd is a temperature value of an environment where the server node is located, sd is a humidity value of the environment where the server node is located, bw is a working environment temperature value recommended by a server manufacturer document, bs is a working environment humidity value recommended by the server manufacturer document, c1 and c2 are set proportionality coefficients, and both c1 and c2 are larger than 0;
Step S22: performing abnormality judgment according to the energy consumption ratio data;
step S23: respectively transmitting the server nodes corresponding to the normal state and the server nodes corresponding to the abnormal state to a data processing module as abnormal processing data;
in the step S22, the abnormality determination is performed according to the energy consumption ratio data, and the specific steps are as follows:
step S221: acquiring full load data of a server node and rated power consumption data of the server according to a server manufacturer document, and acquiring minimum data of the server node and reference power consumption data of the server according to the server manufacturer document;
step S222: calculating to obtain energy consumption ratio threshold value data Nb1 of the server node according to full load data of the server node, reference power consumption data of the server node, temperature values of environments where the server node is located, humidity values of the environments where the server node is located, working environment temperature values recommended by a server manufacturer document and working environment humidity values recommended by the server manufacturer document;
step S223: calculating to obtain energy consumption ratio threshold value data Nb2 of the server node according to the empty load data of the server node, rated power consumption data of the server node, temperature value of the environment where the server node is located, humidity value of the environment where the server node is located, working environment temperature value recommended by a server manufacturer document and working environment humidity value recommended by the server manufacturer document;
Step S224: obtaining abnormal processing data according to the energy consumption ratio threshold value data of the server node and the energy consumption ratio data of the server node:
when Nb2 is more than 0 and less than Nb1, the server node is in a normal state;
when Nb is more than or equal to Nb1 and more than 0 or less than Nb2, the server node is in an abnormal state;
in the step S3, data processing is performed according to the basic data and the exception handling data of the data center, and the specific steps are as follows:
step S31: the energy consumption ratio grading is carried out on the server nodes in the normal state, and the specific steps are as follows:
step S311: acquiring server node energy consumption ratio data in a normal state through exception processing data;
step S312: randomly selecting u server nodes of a data center, acquiring the energy consumption ratio of the u servers, calculating the average energy consumption ratio of the u server nodes through the energy consumption ratio acquisition of the u servers, and setting the average energy consumption ratio as server node energy consumption ratio threshold data;
step S313: and judging the energy consumption ratio of the server node in a normal state, wherein the energy consumption ratio is specifically as follows:
when Nb is more than or equal to Nbp, judging that the node is a service node with high energy consumption ratio;
when Nbp is more than Nb and more than or equal to 0, judging that the node is a service node with low energy consumption ratio;
step S314: setting the judgment result of the step S313 as server node energy consumption ratio grading data;
Step S32: compressing the network data;
in the step S4, the energy consumption management of the server node is completed, and the specific steps are as follows:
step S41: the abnormal server node is subjected to abnormal management, and the specific steps are as follows:
step S411: the method comprises the steps of receiving exception handling data, obtaining a work log of a server node in an exception state, and determining the exception reason of the server node according to the work log, wherein the exception reason of the server node comprises hardware faults and software faults;
step S412: aiming at hardware faults, server node maintenance personnel are arranged to overhaul and replace corresponding hardware, so that the server node returns to a normal working state;
step S413: aiming at software faults, updating a software system of the server node, repairing a configuration file and reinstalling components to enable the server node to recover to a normal working state;
step S42: performing energy consumption management on the server node;
in the step S42, the energy consumption management of the server node is completed, and the specific steps are as follows:
step S421: obtaining energy consumption ratio grading data of a server node;
step S422: aiming at the high energy consumption ratio service node, the energy consumption management mode is specifically as follows:
Step S4221: the method comprises the steps that configuration information and load states of a server node are obtained through a task manager, and a virtual machine is utilized to clone a configuration file and a network file of the current server node;
step S4222: selecting a cloud server according to the load condition of the server node, creating a cloud server which is the same as the configuration information of the server node in the cloud server, and applying the configuration of the server node to the mirror image of the cloud server through the configuration information;
step S4223: transmitting network data of the server node to a cloud server through file transmission service of the cloud server for cloud storage, importing a mirror image into the cloud server, setting a network and configuring a security policy;
step S4224: running a test workload on a cloud server, verifying the performance and stability of the migrated system, and ensuring the normal running and response of the migrated virtual machine and application program;
step S423: aiming at the low energy consumption ratio service ratio node, the energy consumption management mode is specifically as follows:
step S4231: the DCIM is used for monitoring the server node with low energy consumption ratio in real time, and identifying the energy consumption peak and the energy consumption valley of the server node;
step S42311: aiming at energy consumption peaks, the management strategy for the server node is specifically as follows:
Step S423111: the server nodes distribute the requests to different server nodes through a load balancing strategy, so that overload of a certain node is avoided, and the overall operation efficiency is improved;
step S423112: a dynamic power consumption adjustment strategy is adopted for the server node, computing resources are provided according to actual requirements and load conditions, and the processor frequency of a server node assembly is reduced;
step S42312: aiming at the low energy consumption, the management strategy for the server node is specifically as follows:
step S423121: CPU and memory resources of the server node are reduced, resources are allocated according to the needs, and energy consumption of the server node is reduced;
step S423121: and increasing the load monitoring frequency of the server nodes, merging tasks of the server nodes with the working frequency of the processor, and closing part of the server nodes.
2. The method for managing energy consumption of a data center based on energy consumption data according to claim 1, wherein in the step S1, basic data of the data center is obtained, and the specific steps are as follows:
step S11: acquiring energy consumption data of a server node;
step S12: load data of a server node is obtained;
step S13: acquiring a temperature value of an environment where the server node is located through a temperature sensor, acquiring a humidity value of the environment where the server node is located through a humidity sensor, and setting the temperature value and the humidity value of the environment where the server node is located as environment data;
Step S14: acquiring hardware use data, message queues, log records, monitoring data and other data transmitted by the server node mutually, and setting the data as network data;
step S15: and setting the energy consumption data of the server node, the load data of the server node, the environment data and the network data as data center basic data.
3. The method for managing energy consumption of a data center based on energy consumption data according to claim 2, wherein in step S11, energy consumption data of a server node is obtained, and the specific steps are as follows:
step S111: q time nodes are obtained through a time counter, current data of the q time nodes through a server node are obtained through a current sensor, voltage data of the q time nodes are obtained through a voltage sensor, and a power factor of the server node is obtained through a power quality analyzer;
step S112: calculating to obtain an average current value of the server node during working by using the current data of the q time nodes through the server node and the power factor of the server node;
step S113: calculating to obtain an average voltage value of the server node during working by using the q time nodes through the voltage data of the server node and the power factor of the server node;
Step S114: calculating to obtain energy consumption data of the server node according to the average current value of the server node during operation, the average voltage value of the server node during operation, the operation time length data of the server node and the power factor of the server node;
according to the formulaCalculating to obtain energy consumption data Pg of the server node;
pg is energy consumption data of a server node, ip is an average current value when the server node works, vp is an average voltage value when the server node works, tg is working time length data of the server node, wy is a power factor of the server node, a1 is a set power factor proportional coefficient, and a1 is larger than 0.
4. The method for managing energy consumption of a data center based on energy consumption data according to claim 2, wherein in step S12, load data of a server node is obtained, and the specific steps are as follows:
step S121: dividing the working time length data into m identical time units according to seconds, and respectively acquiring CPU utilization rate, memory utilization rate and network flow of server nodes corresponding to the m identical time units through a task manager;
step S122: calculating to obtain the average CPU utilization rate of the server node according to the CPU utilization rates of the server nodes corresponding to the m same time units, calculating to obtain the average memory utilization rate of the server node according to the memory utilization rates of the server nodes corresponding to the m same time units, and calculating to obtain the average network flow of the server node according to the network flow of the server nodes corresponding to the m same time units;
Step S123: calculating to obtain load data of the server node according to the average CPU utilization rate of the server node, the average memory utilization rate of the server node and the average network flow of the server node;
according to the formulaCalculating to obtain load data of the server node;
fz is load data of the server node, cp is average CPU utilization rate Cp of the server node, np is average memory utilization rate of the server node, lp is average network traffic of the server node, tg is working time length data of the server node, b1 and b2 are set proportionality coefficients, and b1 and b2 are both greater than 0.
5. The method for managing energy consumption of a data center based on energy consumption data according to claim 1, wherein in the step S32, the compression processing is performed on the network data, and the specific steps are as follows:
step S321: the transmission node of the network data comprises a server node of a sending end and a server node of a receiving end, when the server node of the sending end sends an HTTP request to the server node of the receiving end, an Accept-Encoding field is added in a request header, and after receiving the request, the server node of the receiving end judges whether the Gzip compression is supported or not;
step S322: if so, adding a Content-Encoding field in the response header, and transmitting the network data compressed by the Gzip to a server node of the receiving end by a server node of the transmitting end;
Step S3221: using a character string matching algorithm to acquire repeated character strings in the network data, replacing the repeated character strings in the network data by character strings based on Lempel-Ziv-Welch (LZW), and replacing the repeated character strings with short pointers and length codes so as to reduce the byte number of the repeated character strings;
step S3221: compression encoding is carried out on the replaced short pointer and the replaced length code by using Huffman coding, so as to complete Gzip compression of network data;
step S323: if not, the server node of the transmitting end transmits the uncompressed network data to the server node of the receiving end.
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