CN117411885A - Data center-based network information data transmission method and system - Google Patents
Data center-based network information data transmission method and system Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/101—Server selection for load balancing based on network conditions
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1023—Server selection for load balancing based on a hash applied to IP addresses or costs
Abstract
The invention belongs to the technical fields of communication engineering, data centers and computation, and provides a data center-based network information data transmission method and system, which specifically comprises the following steps: firstly, arranging an energy-saving network transmission scene, secondly, obtaining hash codes and total flow from the energy-saving network transmission scene and forming a binary group, taking the binary group as redundancy load, performing overflow analysis through the redundancy load, calculating to obtain node overflow, and finally, performing resource allocation on node equipment of a data center through the node overflow. And performing transverse comparison on all the node devices in time, thereby effectively quantifying the redundancy of the corresponding node device to other node devices or controlling the rationality of the redundant information output of the node devices, further enhancing the rationality of the data center for the resource or energy distribution among the node devices, reducing unnecessary resource occupation and energy consumption and achieving the energy saving effect.
Description
Technical Field
The invention belongs to the technical fields of communication engineering, data centers and computation, and particularly relates to a data center-based network information data transmission method and system.
Background
With the popularity and application of the internet, data centers for centrally storing, processing, and managing large-scale data and computing resources have become an important component of modern society; the data center is required to manage and monitor different nodes, the nodes of the data center comprise servers, storage devices, network devices, virtual machines or containers, the nodes transmit a large amount of data to the data center at any moment, and a large amount of information transmission needs to consume a large amount of energy, so the data center often needs extremely high energy consumption, in the process of designing a data center, the energy consumption of the data center is taken as an important inspection standard, however, in the practical application scene of the data center, a plurality of redundant nodes exist, particularly in a plurality of large data centers, the redundant nodes transmit information with the same characteristics or poor characteristics as those of other nodes, but the contained information is not as comprehensive as that of the other nodes, so the node is recorded as the redundant node in the time period, and therefore, how to reasonably allocate the energy to the redundant node has a challenge in reducing the energy consumption ratio in the information transmission.
Disclosure of Invention
The invention aims to provide a data center-based network information data transmission method and system, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
To achieve the above object, according to an aspect of the present invention, there is provided a data center-based network information transmission data method, the method comprising the steps of:
s100, arranging an energy-saving network transmission scene;
s200, obtaining a hash offset value and total flow from an energy-saving network transmission scene, and taking the ratio of the hash offset value and the total flow as redundancy load;
s300, performing overflow analysis through redundancy load, and calculating to obtain node overflow;
s400, performing resource allocation on node equipment of the data center through the node time overflow.
Further, in step S100, the method for arranging the energy-saving network transmission scenario is: arranging an energy-saving network transmission scene which comprises a data center and a plurality of node devices; the node equipment comprises a server, a storage device, network equipment or a virtual machine, and transmits information to the data center; the data center comprises an enterprise cloud data center, an edge business data center, a cooperation data center or a private data center, one data center is used for controlling a plurality of node devices, the data center is used for receiving information transmitted by the node devices and analyzing the information, and the power of the node devices is adjusted according to analysis results.
Further, in step S200, the method for obtaining the hash offset value and the total traffic from the energy-saving network transmission scenario and using the ratio of the hash offset value and the total traffic as the redundancy load is as follows: recording the flow transmitted to the data center by any node equipment in the current period as the total flow of the node equipment, wherein the total flow in a period of time is the sum of the size of the uploading data flow and the size of the downloading data flow; setting a time period TL, TL epsilon [0.5,5] min, receiving information from each node device by a data center in the latest TL period, mapping the information input by any node device into a hash code with a fixed length by utilizing a hash function, wherein the hash function used by the data center is any one of MD5, SHA-1, SHA-256 or SHA-3, setting the hash function used by default as MD5, and calculating to obtain the Hamming distance between two node devices according to the hash codes of the node device and any other node device, wherein the Hamming distance is obtained by a Hamming distance algorithm; recording an average value of Hamming distances between the node equipment and other node equipment as a Hash offset value; the total flow of information received from any node device in the TL period is obtained by a network flow analysis tool, and the network flow analysis tool obtains the total flow by analyzing the received information, wherein the network flow analysis tool is one of Wireshark, tcpdump, ntop or Surica; the ratio of the hash offset value and the total flow of any node device in the TL period is taken as the redundancy load of the node device.
Further, in step S300, the method for calculating the node time overflow degree by performing the time overflow analysis on the redundancy load is as follows: presetting a monitoring interval TimeSK, wherein the TimeSK is epsilon [30,120] minutes, acquiring redundancy load of each moment in the latest TimeSK, and recording the maximum value as a first peak value; taking the ratio of the redundancy load at the current moment to the first peak value as the load depth DBG at the moment;
acquiring all load depths in the latest TimeSK to form a sequence as a load depth sequence; the time when the maximum value appears in the load depth sequence and the next time are respectively marked as tk m And tk l The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first element in the default payload depth sequence is tk l The method comprises the steps of carrying out a first treatment on the surface of the Definition of any one tk l To the first tk appearing thereafter m The time interval between the two is used as a load depth interval; taking the average value of the load depth at each moment in a load depth interval as the performance level DBG of the load depth interval avg The energy flow intensity QUA at the j1 th time is calculated by taking j1 as the sequence number of the time j1 The method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base, mean { } is an average function, j2 and kmx are the sequence number and total number of the load depth interval respectively, and DBG is used avg [j2]Representing the performance level of the j2 nd load depth interval; the Sc (j 1) is a section retrieving function, a calling element is a time mark, and a sequence number of a load depth section corresponding to the j1 th time is obtained through the section retrieving function; with DBG avg [Sc(j1)]Representing the performance level of the corresponding load depth interval when the moment mark is j 1;
and forming a sequence by using all the energy flow intensities obtained from the current moment as an energy flow intensity sequence, and recording the difference between the maximum value and the minimum value as a load intensity domain, wherein the node time overflow degree at the current moment is the ratio of the energy flow intensity to the load intensity domain.
Because the long-time non-updating phenomenon easily occurs in the process of acquiring the first peak value of the redundancy load maximum value, especially, the problem that the peak value acquired in the data transmission peak period causes insufficient sensitivity to the load depth calculation acquired in the monitoring interval where the peak value is positioned, the accuracy of overflow degree is insufficient especially in the time point, and the rationality of resource allocation of node equipment is further affected. However, the problem that the sensitivity of the first peak value to the load depth calculation is insufficient cannot be solved in the prior art, in order to achieve a more reliable quantization effect and obtain a more accurate node time overflow degree, the applicability or adaptability of the first peak value is wider, and the phenomenon of insufficient sensitivity is eliminated, so that the invention provides a more preferable scheme:
preferably, in step S300, the method for calculating the node time overflow by performing time overflow analysis on the redundancy load is as follows: recording redundancy load as RDL, setting time period tg, tg epsilon [100,150] minutes, taking the ratio of the difference value between the larger value and the smaller value in the redundancy load at any moment and the redundancy load at the moment before the moment as the relative chargeability difference at the moment, and calculating and obtaining the corresponding relative chargeability difference for all redundancy loads; in the latest tg time period, taking redundancy loads of different time under the same node equipment as one row, and taking redundancy loads of different node equipment under the same time as one column, constructing a matrix to be recorded as a lotus-like model, so that each node equipment has a corresponding row with the lotus-like model; the average value and the maximum value of any column element in the lotus pseudo model are respectively marked as point redundancy level EMV and point redundancy high-order HMV at corresponding moments;
the average value of the relative bad chargeability of all elements in the chargeability simulation model is recorded as a modal value; any node device is taken as a current node: if the relative chargeability difference of the current node at a moment is larger than the modal value, the moment is marked as the overmodal point of the current node, otherwise, the overmodal point is the hidden temporal point; starting inverse time search from the current moment, taking a plurality of overmodal points which are continuous in time sequence as an overmodal section in the latest tg time section, taking the minimum value of the relative charge differences of all moments in the modal section as an overmodal threshold value, traversing the relative charge differences of all moments from the most distant moment inverse time of the overmodal section until the relative charge differences of a moment are larger than or equal to the overmodal threshold value Fstdv, and combining all the traversed moments with all the overmodal sections into a simulated analysis section;
the most distant moment of the overmodal section refers to the moment farthest from the current moment in each moment of the overmodal section;
intercepting matrixes corresponding to each simulated analysis section from the lotus simulated model to be used as derivative matrixes; the corresponding behavior of the current node in the derivative matrix is a main subordinate row; the aging parameter dur of the lotus-based simulation model is the moment difference between the moment of the modal threshold value passing through the corresponding simulation analysis section and the current moment; wherein the time difference is the number of times between two times;
the partial side index MBIdx is obtained through the overmodal threshold value of the derivative matrix and the calculation of the dot redundancy level and the high redundancy load at each moment, and the calculation method comprises the following steps:
；
wherein i1 is a cumulative variable, inum is the number of columns of the derivative matrix, exp () is an exponential function with a natural constant e as a base; HMV (HMV) i1 And EMV i1 Respectively representing the corresponding dot redundancy level and the dot redundancy high bit of the ith column 1;
constructing a sequence corresponding to redundancy load of each overmodal point in the main subordinate line and marking the sequence as a load analysis sequence MOT_Ls; according to each derivative matrix of the current node, calculating the node time overflow NMOD, wherein the calculation method comprises the following steps:
；
where k1 is the accumulated variable, knum is the number of derivative matrices, MBIdx k1 And dur k1 Represents the partial side index and the aging parameter value, avg of the kth 1 derivative matrix respectively<>As an arithmetic mean function, ds<>The return value of the pole difference function is the difference between the maximum value and the minimum value in the call sequence.
The beneficial effects are that: the node overflow is the quantitative calculation of hash offset values and total flow of different node devices, and by transversely comparing all the node devices in time, the redundancy of the corresponding node devices to other node devices or the rationality of controlling the output of redundant information of the node devices is effectively quantized, the weight of the node devices with similar hash codes and small total flow in a model is increased, the sensitivity of identifying the node devices with similar hash codes is enhanced, the accuracy of judging redundant nodes is further improved, the risk of consistence of information hash codes with different characteristics due to hash collision is reduced, and reliable mathematical support is provided for further obtaining accurate redundant nodes and accurately controlling the power of the redundant nodes.
Further, in step S400, the method for adjusting the node device of the data center by the node time overflow is: the average value and standard deviation of the node time overflow of all node equipment are respectively recorded as a domain redundancy ratio FRCC and a redundancy standard deviation RCSD, a numerical interval is set as a redundancy analysis domain RACF, the RACF is E [ FRCC-2. RCSD, FRCC+2. RCSD ], and if the node time overflow of one node equipment is larger than the redundancy analysis domain interval, the transmission power of the node equipment is reduced by 10% -15%; if the node time overflow of a node device is in a redundancy analysis domain interval, reducing the transmission power of the node device by 5% -10%; if the node time overflow of a node device is smaller than the redundancy analysis domain interval, the node device keeps the original power transmission information.
Preferably, all undefined variables in the present invention, if not explicitly defined, may be thresholds set manually.
The invention also provides a data center-based network information transmission data system, which comprises: the steps of the data center-based network information transmission data method are realized when the processor executes the computer program, the data center-based network information transmission data system can be operated in a desktop computer, a notebook computer, a palm computer, a cloud data center and other computing devices, and the operable system can comprise, but is not limited to, a processor, a memory and a server cluster, and the processor executes the computer program to operate in the following units of the system:
a network transmission scene arrangement unit for arranging energy-saving network transmission scenes
The data value acquisition unit is used for acquiring a hash offset value and total flow from an energy-saving network transmission scene, and taking the ratio of the hash offset value and the total flow as redundancy load;
the model building unit is used for carrying out overflow analysis through redundancy load and calculating to obtain node overflow;
and the resource adjusting unit is used for carrying out resource allocation on the node equipment of the data center through the node time overflow.
The beneficial effects of the invention are as follows: the invention provides a data center-based network information transmission data method and system, which carry out energy-saving adjustment on information transmission of node equipment of a data center by utilizing node time overflow degree of the node equipment, wherein the node time overflow degree is quantitative calculation on hash offset values and total flow of different node equipment, and by carrying out transverse comparison on all the node equipment in time, the redundancy degree of the corresponding node equipment on other node equipment or the rationality of controlling redundant information output of the node equipment is effectively quantized, the weight of the node equipment with similar hash codes and small total flow in a model is increased, thereby enhancing the sensitivity of identifying the node equipment with similar hash codes, further improving the accuracy of judging redundant nodes, reducing the risk of consistent information hash codes with different characteristics due to hash collision, providing reliable mathematical support for further obtaining accurate redundant nodes and accurately controlling the power of the redundant nodes, further enhancing the rationality of resource or energy allocation among the node equipment by the data center, reducing unnecessary resource occupation and energy consumption and achieving the energy saving effect.
Drawings
The above and other features of the present invention will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present invention, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of a method for transmitting data based on network information of a data center;
fig. 2 is a diagram showing a structure of a network information transmission data system based on a data center.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
Referring to fig. 1, which is a flowchart illustrating a data center-based network information transmission data method, a data center-based network information transmission data method according to an embodiment of the present invention will be described with reference to fig. 1, and the method includes the following steps:
s100, arranging an energy-saving network transmission scene;
s200, obtaining a hash offset value and total flow from an energy-saving network transmission scene, and taking the ratio of the hash offset value and the total flow as redundancy load;
s300, performing overflow analysis through redundancy load, and calculating to obtain node overflow;
s400, performing resource allocation on node equipment of the data center through the node time overflow.
Further, in step S100, the method for arranging the energy-saving network transmission scenario is: arranging an energy-saving network transmission scene which comprises a data center and a plurality of node devices; the node equipment comprises a server, a storage device, network equipment or a virtual machine, and transmits information to the data center; the data center comprises an enterprise cloud data center, an edge business data center, a cooperation data center or a private data center, one data center is used for controlling a plurality of node devices, the data center is used for receiving information transmitted by the node devices and analyzing the information, and the power of the node devices is adjusted according to analysis results.
Further, in step S200, the method for obtaining the hash offset value and the total traffic from the energy-saving network transmission scenario and using the ratio of the hash offset value and the total traffic as the redundancy load is as follows: recording the flow transmitted to the data center by any node equipment in the current period as the total flow of the node equipment, wherein the total flow in a period of time is the sum of the size of the uploading data flow and the size of the downloading data flow; setting a time period TL, TL epsilon [0.5,5] min, receiving information from each node device by a data center in the latest TL period, mapping the information input by any node device into a hash code with a fixed length by utilizing a hash function, wherein the hash function used by the data center is any one of MD5, SHA-1, SHA-256 or SHA-3, setting the hash function used by default as MD5, and calculating to obtain the Hamming distance between two node devices according to the hash codes of the node device and any other node device, wherein the Hamming distance is obtained by a Hamming distance algorithm; recording an average value of Hamming distances between the node equipment and other node equipment as a Hash offset value; the total flow of information received from any node device in the TL period is obtained by a network flow analysis tool, and the network flow analysis tool obtains the total flow by analyzing the received information, wherein the network flow analysis tool is one of Wireshark, tcpdump, ntop or Surica; the ratio of the hash offset value and the total flow of any node device in the TL period is taken as the redundancy load of the node device.
Further, in step S300, the method for calculating the node time overflow degree by performing the time overflow analysis on the redundancy load is as follows: presetting a monitoring interval TimeSK, wherein the TimeSK is epsilon [30,120] minutes, acquiring redundancy load of each moment in the latest TimeSK, and recording the maximum value as a first peak value; taking the ratio of the redundancy load at the current moment to the first peak value as the load depth DBG at the moment;
obtaining all load depths in the latest TimeSK to form a sequence as negativeCarrying a depth sequence; the time when the maximum value appears in the load depth sequence and the next time are respectively marked as tk m And tk l The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first element in the default payload depth sequence is tk l The method comprises the steps of carrying out a first treatment on the surface of the Definition of any one tk l To the first tk appearing thereafter m The time interval between the two is used as a load depth interval; taking the average value of the load depth at each moment in a load depth interval as the performance level DBG of the load depth interval avg The energy flow intensity QUA at the j1 th time is calculated by taking j1 as the sequence number of the time j1 The method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base, mean { } is an average function, j2 and kmx are the sequence number and total number of the load depth interval respectively, and DBG is used avg [j2]Representing the performance level of the j2 nd load depth interval; the Sc (j 1) is a section retrieving function, a calling element is a time mark, and a sequence number of a load depth section corresponding to the j1 th time is obtained through the section retrieving function; with DBG avg [Sc(j1)]Representing the performance level of the corresponding load depth interval when the moment mark is j 1;
and forming a sequence by using all the energy flow intensities obtained from the current moment as an energy flow intensity sequence, and recording the difference between the maximum value and the minimum value as a load intensity domain, wherein the node time overflow degree at the current moment is the ratio of the energy flow intensity to the load intensity domain.
Preferably, in step S300, the method for calculating the node time overflow by performing time overflow analysis on the redundancy load is as follows: recording redundancy load as RDL, setting time period tg, tg epsilon [100,150] minutes, taking the ratio of the difference value between the larger value and the smaller value in the redundancy load at any moment and the redundancy load at the moment before the moment as the relative chargeability difference at the moment, and calculating and obtaining the corresponding relative chargeability difference for all redundancy loads; in the latest tg time period, taking redundancy loads of different time under the same node equipment as one row, and taking redundancy loads of different node equipment under the same time as one column, constructing a matrix to be recorded as a lotus-like model, so that each node equipment has a corresponding row with the lotus-like model; the average value and the maximum value of any column element in the lotus pseudo model are respectively marked as point redundancy level EMV and point redundancy high-order HMV at corresponding moments;
the average value of the relative bad chargeability of all elements in the chargeability simulation model is recorded as a modal value; any node device is taken as a current node: if the relative chargeability difference of the current node at a moment is larger than the modal value, the moment is marked as the overmodal point of the current node, otherwise, the overmodal point is the hidden temporal point; starting inverse time search from the current moment, taking a plurality of overmodal points which are continuous in time sequence as an overmodal section in the latest tg time section, taking the minimum value of the relative charge differences of all moments in the modal section as an overmodal threshold value, traversing the relative charge differences of all moments from the most distant moment inverse time of the overmodal section until the relative charge differences of a moment are larger than or equal to the overmodal threshold value Fstdv, and combining all the traversed moments with all the overmodal sections into a simulated analysis section;
the most distant moment of the overmodal section refers to the moment farthest from the current moment in each moment of the overmodal section;
intercepting matrixes corresponding to each simulated analysis section from the lotus simulated model to be used as derivative matrixes; the corresponding behavior of the current node in the derivative matrix is a main subordinate row; the aging parameter dur of the lotus-based simulation model is the moment difference between the moment of the modal threshold value passing through the corresponding simulation analysis section and the current moment; wherein the time difference is the number of times between two times;
the partial side index MBIdx is obtained through the overmodal threshold value of the derivative matrix and the calculation of the dot redundancy level and the high redundancy load at each moment, and the calculation method comprises the following steps:
；
wherein i1 is a cumulative variable, jnum is the number of columns of the derivative matrix, exp () is an exponential function with a natural constant e as a base;
constructing a sequence corresponding to redundancy load of each overmodal point in the main subordinate line and marking the sequence as a load analysis sequence MOT_Ls; according to each derivative matrix of the current node, calculating the node time overflow NMOD, wherein the calculation method comprises the following steps:
；
where k1 is the accumulated variable, knum is the number of derivative matrices, MBIdx k1 And dur k1 Represents the partial side index and the aging parameter value, avg of the kth 1 derivative matrix respectively<>As an arithmetic mean function, ds<>The return value of the pole difference function is the difference between the maximum value and the minimum value in the call sequence.
Further, in step S400, the method for adjusting the node device of the data center by the node time overflow is: the average value and standard deviation of the node time overflow of all node equipment are respectively recorded as a domain redundancy ratio FRCC and a redundancy standard deviation RCSD, a numerical interval is set as a redundancy analysis domain RACF, the RACF is E [ FRCC-2. RCSD, FRCC+2. RCSD ], and if the node time overflow of one node equipment is larger than the redundancy analysis domain interval, the transmission power of the node equipment is reduced by 10% -15%; if the node time overflow of a node device is in a redundancy analysis domain interval, reducing the transmission power of the node device by 5% -10%; if the node time overflow of a node device is smaller than the redundancy analysis domain interval, the node device keeps the original power transmission information.
The embodiment of the invention provides a data center-based network information transmission data system, as shown in fig. 2, which is a structure diagram of the data center-based network information transmission data system, and the embodiment of the invention comprises: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in one embodiment of a data center based network information transfer data system described above when the computer program is executed.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the following system:
a network transmission scene arrangement unit for arranging energy-saving network transmission scenes
The data value acquisition unit is used for acquiring a hash offset value and total flow from an energy-saving network transmission scene, and taking the ratio of the hash offset value and the total flow as redundancy load;
the model building unit is used for carrying out overflow analysis through redundancy load and calculating to obtain node overflow;
and the resource adjusting unit is used for carrying out resource allocation on the node equipment of the data center through the node time overflow.
The network information transmission data system based on the data center can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The data center-based network information transmission data system may include, but is not limited to, a processor and a memory. It will be appreciated by those skilled in the art that the example is merely an example of a data center based network information transfer data system and is not limiting of a data center based network information transfer data system, and may include more or fewer components than examples, or may combine certain components, or different components, e.g., the data center based network information transfer data system may further include input and output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor is a control center of the data center-based network information transmission data system operation system, and various interfaces and lines are used to connect various parts of the entire data center-based network information transmission data system operation system.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the data center-based network information transmission data system by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
Claims (7)
1. A data center-based network information transmission data method, the method comprising the steps of:
s100, arranging an energy-saving network transmission scene;
s200, obtaining a hash offset value and total flow from an energy-saving network transmission scene, and taking the ratio of the hash offset value and the total flow as redundancy load;
s300, performing overflow analysis through redundancy load, and calculating to obtain node overflow;
s400, performing resource allocation on node equipment of the data center through the node time overflow degree;
comparing redundant loads in a period of time to obtain a peak value first peak value, calculating current load depth through the first peak value, combining the load depths at all times to form a load depth sequence, screening peak value time points and valley value time points according to the load depth sequence, dividing a load depth interval through the peak value time points and the valley value time points, calculating corresponding performance levels, calculating corresponding energy flow intensity according to the performance levels of the load depth interval, finally obtaining a load intensity domain according to the energy flow intensity at all times, and taking the ratio of the current energy flow intensity to the load intensity domain as node time overflow.
2. The method for transmitting data based on network information of data center according to claim 1, wherein in step S100, the method for arranging energy-saving network transmission scenarios is: arranging an energy-saving network transmission scene which comprises a data center and a plurality of node devices; the node equipment comprises a server, a storage device, network equipment or a virtual machine, and transmits information to the data center; the data center comprises an enterprise cloud data center, an edge business data center, a cooperation data center or a private data center, one data center is used for controlling a plurality of node devices, the data center is used for receiving information transmitted by the node devices and analyzing the information, and the power of the node devices is adjusted according to analysis results.
3. The method for transmitting data based on network information of claim 1, wherein in step S200, the method for obtaining the hash offset value and the total traffic from the energy-saving network transmission scenario and using the ratio of the hash offset value and the total traffic as the redundancy load is as follows: recording the flow transmitted to the data center by any node equipment in the current period as the total flow of the node equipment, wherein the total flow in a period of time is the sum of the size of the uploading data flow and the size of the downloading data flow; setting a time period TL, TL epsilon [0.5,5] min, receiving information from each node device by a data center in the latest TL period, mapping the information input by any node device into a hash code with a fixed length by utilizing a hash function, wherein the hash function used by the data center is any one of MD5, SHA-1, SHA-256 or SHA-3, setting the hash function used by default as MD5, and calculating to obtain the Hamming distance between two node devices according to the hash codes of the node device and any other node device, wherein the Hamming distance is obtained by a Hamming distance algorithm; recording an average value of Hamming distances between the node equipment and other node equipment as a Hash offset value; the total flow of information received from any node device in the TL period is obtained by a network flow analysis tool, and the network flow analysis tool obtains the total flow by analyzing the received information, wherein the network flow analysis tool is one of Wireshark, tcpdump, ntop or Surica; the ratio of the hash offset value and the total flow of any node device in the TL period is taken as the redundancy load of the node device.
4. The method for transmitting data based on network information of claim 1, wherein in step S300, the method for obtaining node time overflow by performing time overflow analysis through redundancy load is: presetting a monitoring interval TimeSK, wherein the TimeSK is epsilon [30,120] minutes, acquiring redundancy load of each moment in the latest TimeSK, and recording the maximum value as a first peak value; taking the ratio of the redundancy load at the current moment to the first peak value as the load depth DBG at the moment;
acquiring all load depths in the latest TimeSK to form a sequence as a load depth sequence; the time when the maximum value appears in the load depth sequence and the next time are respectively marked as tk m And tk l The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first element in the default payload depth sequence is tk l The method comprises the steps of carrying out a first treatment on the surface of the Definition of any one tk l To the first tk appearing thereafter m The time interval between the two is used as a load depth interval; with the load depth at each moment in a load depth intervalAverage value of the degrees as the performance level DBG of the load depth interval avg The energy flow intensity QUA at the j1 th time is calculated by taking j1 as the sequence number of the time j1 The method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base, mean { } is an average function, j2 and kmx are the sequence number and total number of the load depth interval respectively, and DBG is used avg [j2]Representing the performance level of the j2 nd load depth interval; the Sc (j 1) is a section retrieving function, a calling element is a time mark, and a sequence number of a load depth section corresponding to the j1 th time is obtained through the section retrieving function; with DBG avg [Sc(j1)]Representing the performance level of the corresponding load depth interval when the moment mark is j 1;
and forming a sequence by using all the energy flow intensities obtained from the current moment as an energy flow intensity sequence, and recording the difference between the maximum value and the minimum value as a load intensity domain, wherein the node time overflow degree at the current moment is the ratio of the energy flow intensity to the load intensity domain.
5. The method for transmitting data based on network information of claim 1, wherein in step S300, the method for obtaining node time overflow by performing time overflow analysis through redundancy load is: recording redundancy load as RDL, setting time period tg, tg epsilon [100,150] minutes, taking the ratio of the difference value between the larger value and the smaller value in the redundancy load at any moment and the redundancy load at the moment before the moment as the relative chargeability difference at the moment, and calculating and obtaining the corresponding relative chargeability difference for all redundancy loads; in the latest tg time period, taking redundancy loads of different time points of the same node equipment as one row, and taking redundancy loads of different node equipment at the same time point as one column, and constructing a matrix to be recorded as a lotus nature simulation model; respectively marking the average value and the maximum value of any column element in the lotus pseudo model as the dot redundancy level and the dot redundancy high bit at the corresponding moment;
the average value of the relative bad chargeability of all elements in the chargeability simulation model is recorded as a modal value; any node device is taken as a current node: if the relative chargeability difference of the current node at a moment is larger than the modal value, the moment is marked as the overmodal point of the current node, otherwise, the overmodal point is the hidden temporal point; starting inverse time search from the current moment, taking a plurality of overmodal points which are continuous in time sequence as an overmodal section in the latest tg time section, taking the minimum value of the relative charge differences of all moments in the modal section as an overmodal threshold value, traversing the relative charge differences of all moments from the most distant moment inverse time of the overmodal section until the relative charge differences of a moment appear to be more than or equal to the overmodal threshold value, and combining all moments of the traversing and all moments of the overmodal section into a simulated analysis section;
intercepting matrixes corresponding to each simulated analysis section from the lotus simulated model to be used as derivative matrixes; the corresponding behavior of the current node in the derivative matrix is a main subordinate row; and obtaining the partial side index of the node according to the overmodal threshold of the derivative matrix and the dot redundancy level and the high redundancy load at each moment of the overmodal threshold, and calculating the node time overflow according to the partial side index corresponding to each derivative matrix of the current node.
6. The method for transmitting data based on network information of data center according to claim 1, wherein in step S400, the method for adjusting node equipment of the data center by node time overflow is: the average value and standard deviation of the node time overflow of all node equipment are respectively recorded as a domain redundancy ratio FRCC and a redundancy standard deviation RCSD, a numerical interval is set as a redundancy analysis domain RACF, the RACF is E [ FRCC-2. RCSD, FRCC+2. RCSD ], and if the node time overflow of one node equipment is larger than the redundancy analysis domain interval, the transmission power of the node equipment is reduced by 10% -15%; if the node time overflow of a node device is in a redundancy analysis domain interval, reducing the transmission power of the node device by 5% -10%; if the node time overflow of a node device is smaller than the redundancy analysis domain interval, the node device keeps the original power transmission information.
7. A data center-based network information transfer data system, the data center-based network information transfer data system comprising: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in a data center based network information transmission data method according to any one of claims 1 to 6 when the computer program is executed, the data center based network information transmission data system being executed in a computing device of a desktop computer, a notebook computer, a palm computer and a cloud data center.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180248939A1 (en) * | 2017-02-27 | 2018-08-30 | University Of Electronic Science And Technology Of China | Energy-saving deployment method of virtual cdn |
| CN112270550A (en) * | 2020-10-21 | 2021-01-26 | 北京电链科技有限公司 | New energy electric power source tracing method and system based on block chain |
| CN114726676A (en) * | 2022-04-25 | 2022-07-08 | 宁波天擎航天科技有限公司 | Method for processing redundant message in CAN bus dual-channel backup |
| CN116055241A (en) * | 2023-04-03 | 2023-05-02 | 广东海洋大学 | Communication method and system of distributed intelligent home network |
| CN117193503A (en) * | 2023-11-06 | 2023-12-08 | 惠州市鑫晖源科技有限公司 | Heat dissipation host power supply operation monitoring method based on Internet of things |
-
2023
- 2023-12-15 CN CN202311722600.4A patent/CN117411885B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180248939A1 (en) * | 2017-02-27 | 2018-08-30 | University Of Electronic Science And Technology Of China | Energy-saving deployment method of virtual cdn |
| CN112270550A (en) * | 2020-10-21 | 2021-01-26 | 北京电链科技有限公司 | New energy electric power source tracing method and system based on block chain |
| CN114726676A (en) * | 2022-04-25 | 2022-07-08 | 宁波天擎航天科技有限公司 | Method for processing redundant message in CAN bus dual-channel backup |
| CN116055241A (en) * | 2023-04-03 | 2023-05-02 | 广东海洋大学 | Communication method and system of distributed intelligent home network |
| CN117193503A (en) * | 2023-11-06 | 2023-12-08 | 惠州市鑫晖源科技有限公司 | Heat dissipation host power supply operation monitoring method based on Internet of things |
Non-Patent Citations (1)
| Title |
|---|
| 张俊红;王健;孙少军;毕凤荣;李忠鹏;朱传峰;: "基于流固耦合和模态参与因子的低噪声油底壳优化设计研究", 内燃机工程, no. 03, 3 June 2014 (2014-06-03), pages 105 - 110 * |
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