CN110798527A - Node data deployment method, device, system and medium - Google Patents
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Abstract
The invention discloses a node data deployment method, a device, a system and a medium. The method comprises the following steps: acquiring the current flow of a file to be deployed at the current moment and the historical flow of a file to be deployed at the historical moment before the current moment; calculating the flow change trend from the historical flow to the current flow; and generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient. The method can relatively avoid the situation that the deployment of the file to be deployed occupies too much service node resources or cannot meet the access demand of the user terminal, and relatively ensures the overall accuracy of the deployment quantity when the file to be deployed is deployed. In addition, the invention also provides a node data deployment device, a node data deployment system and a node data deployment medium, and the beneficial effects are as described above.
Description
Technical Field
The invention relates to the field of cloud computing, in particular to a node data deployment method, a node data deployment device, a node data deployment system and a node data deployment medium.
Background
With the continuous development of cloud computing, a cloud server architecture based on a CDN network model makes substantial progress in application, and one of the main uses of the current cloud server architecture based on the CDN network model is to provide a corresponding data file according to the access requirements of a user.
The cloud server architecture of the service nodes based on the CDN network mode, that is, the cloud server architecture formed by the individual nodes of a large number of users based on the CDN network mode, and the data files are distributed and deployed in each service node in the form of data segments. When a user accesses a data file through a user terminal, a data access request is firstly initiated to a scheduling node, then the scheduling node acquires index information of the corresponding data file according to the data access request and provides the index information to the user terminal, the index information records service nodes in which the data file is stored in a distributed mode, and after receiving the index information, the user terminal acquires data fragments of the corresponding service nodes according to a service node access path provided in the index information and combines the data fragments into a complete data file.
When a user terminal initiates a data access request to a scheduling node, the scheduling node correspondingly collects the accessed flow of various data files according to the data access request of different data files and provides the collected flow to a deployment node for statistics according to the flow collection result, and then selects the data file with relatively high heat to be deployed in a service node according to the total flow of various data files. Since the number of data files accessed by the user terminal in the future period often changes according to the actual situation, the total traffic of the data files is often required to be adjusted by the redundancy coefficient at present and is used as the deployment number of the data files in the service nodes, but the current redundancy coefficient is usually preset by technicians according to experience, so that the method is difficult to adapt to the scene that the access demand of the current user terminal on the data files is relatively variable, and further, the deployment of the data files may occupy too many service node resources or cannot meet the access demand of the user terminal, and the overall accuracy of the deployment number when the files to be deployed are deployed is difficult to ensure.
Therefore, the problem to be solved by the technical staff is to provide a node data deployment method to relatively ensure the overall accuracy of the deployment quantity when the file to be deployed is deployed.
Disclosure of Invention
The invention aims to provide a node data deployment method, a node data deployment device, a node data deployment system and a node data deployment medium, so as to relatively ensure the overall accuracy of the deployment quantity when a file to be deployed is deployed.
In order to solve the above technical problem, the present invention provides a node data deployment method, including:
acquiring the current flow of a file to be deployed at the current moment and the historical flow of a file to be deployed at the historical moment before the current moment;
calculating the flow change trend from the historical flow to the current flow, wherein the flow change trend is determined according to the flow difference value of the flows at adjacent moments;
and generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient.
Preferably, generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the traffic variation trend includes:
when the flow variation trend is an increasing trend, the initial redundancy coefficient is adjusted upwards to generate a new redundancy coefficient;
when the flow variation trend is a descending trend, the initial redundancy coefficient is adjusted downwards to generate a new redundancy coefficient;
and when the flow variation trend is a steady trend, setting the initial redundancy coefficient as a new redundancy coefficient.
Preferably, when the flow rate trend is an increasing trend, the initial redundancy coefficient is adjusted up to generate a new redundancy coefficient, and the method comprises the following steps:
when the flow variation trend is a growth trend, the amplitude of the growth trend is used as an ascending amplitude weight to adjust the initial redundancy coefficient up to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a descending trend, the initial redundancy coefficient is adjusted downwards to generate a new redundancy coefficient, and the method comprises the following steps:
and when the flow variation trend is a descending trend, taking the amplitude of the descending trend as a descending amplitude weight value to lower the initial redundancy coefficient to generate a new redundancy coefficient.
Preferably, the number of historical time instants is greater than or equal to 2;
correspondingly, when the flow variation trend is a growth trend, the amplitude of the growth trend is used as an ascending amplitude weight to adjust up the initial redundancy coefficient to generate a new redundancy coefficient, which comprises the following steps:
when the flow variation trend is an accelerated growth trend, the amplitude of the growth trend is used as an ascending amplitude weight to adjust the initial redundancy coefficient upwards to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a descending trend, the amplitude of the descending trend is used as a descending amplitude weight value to lower the initial redundancy coefficient to generate a new redundancy coefficient, and the method comprises the following steps:
when the flow variation trend is an accelerated descending trend, the amplitude of the descending trend is used as a descending amplitude weight to lower the initial redundancy coefficient to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a steady trend, the initial redundancy coefficient is set as a new redundancy coefficient, and the method comprises the following steps:
and when the overall variation amplitude of the flow rate variation trend is 0 or when both the ascending trend and the descending trend of the flow rate variation trend exist, setting the initial redundancy coefficient as a new redundancy coefficient.
Preferably, when the overall variation range of the flow rate variation trend is 0 or when both an upward trend and a downward trend of the flow rate variation trend exist, the setting of the initial redundancy coefficient as the new redundancy coefficient includes:
and when the overall variation amplitude of the flow variation trend is 0 or both the ascending trend and the descending trend exist in the flow variation trend and do not exceed the preset fluctuation amplitude, setting the initial redundancy coefficient as a new redundancy coefficient.
Preferably, the initial redundancy coefficient is 1.
In addition, the invention also provides a node data deployment device, which comprises a memory, a processor and a bus, wherein the memory stores a redundancy coefficient generation program which can be transmitted to the processor by the bus and run on the processor, and the redundancy coefficient generation program realizes the node data deployment method when being executed by the processor.
Preferably, the device is a node constituting a CDN network or a blockchain network.
In addition, the invention also provides a node data deployment system, which comprises:
the flow acquiring unit is used for acquiring the current flow of the file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment;
the trend calculation unit is used for calculating a flow change trend from historical flow to current flow, and the flow change trend comprises a flow difference value of the flow at adjacent moments;
and the redundancy coefficient generation module is used for generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient.
Furthermore, the present invention also provides a computer-readable storage medium, on which a redundancy coefficient generation program is stored, where the redundancy coefficient generation program can be executed by one or more processors to implement the node data deployment method as described above.
According to the node data deployment method provided by the invention, the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment are firstly obtained, the flow change trend of the historical flow changing to the current flow is further calculated, and a new redundancy coefficient used for deploying the file to be deployed is further generated based on the flow change trend and the initial redundancy coefficient. The redundancy coefficient is generated according to the flow change trend of the file to be deployed between the historical time and the current time, so that the method can adapt to the scene that the flow of the data file is changed at any time and is relatively variable due to the fact that the access requirement of the current user terminal on the data file is relatively variable, the situation that the deployment of the file to be deployed occupies too many service node resources or cannot meet the access requirement of the user terminal can be relatively avoided, and the overall accuracy of the deployment quantity of the file to be deployed is relatively ensured. In addition, the invention also provides a node data deployment device, a node data deployment system and a node data deployment medium, and the beneficial effects are as described above.
Drawings
In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a node data deployment method according to an embodiment of the present invention;
fig. 2 is a flowchart of another node data deployment method according to an embodiment of the present invention;
fig. 3 is a structural diagram of a node data deployment apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the protection scope of the present invention.
When a user terminal initiates a data access request to a scheduling node, the scheduling node correspondingly collects the accessed flow of various data files according to the data access request of different data files and provides the collected flow to a deployment node for statistics according to the flow collection result, and then selects the data file with relatively high heat to be deployed in a service node according to the total flow of various data files. Since the number of data files accessed by the user terminal in the future period often changes according to the actual situation, the total traffic of the data files is often required to be adjusted by the redundancy coefficient at present and is used as the deployment number of the data files in the service nodes, but the current redundancy coefficient is usually preset by technicians according to experience, so that the method is difficult to adapt to the scene that the access demand of the current user terminal on the data files is relatively variable, and further, the deployment of the data files may occupy too many service node resources or cannot meet the access demand of the user terminal, and the overall accuracy of the deployment number when the files to be deployed are deployed is difficult to ensure.
The core of the invention is to provide a node data deployment method to relatively ensure the overall accuracy of the deployment quantity when the files to be deployed are deployed. In addition, the invention also provides a node data deployment device, a node data deployment system and a node data deployment medium, and the beneficial effects are as described above.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of a node data deployment method according to an embodiment of the present invention. Referring to fig. 1, the specific steps of the node data deployment method include:
step S10: the method comprises the steps of obtaining the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment.
It should be noted that the file to be deployed in this step is a target data file that is selected by the deployment node according to the traffic result of each type of data file and needs to be deployed to the service node, where the target data file is collected and counted by the deployment node according to the preset aggregation period, that is, the file to be deployed is a data file with a higher current popularity, and needs to be deployed to the service node in order to facilitate the user terminal to acquire the data file with the higher popularity. In addition, the current time in this step refers to a time at which the file to be deployed needs to be deployed, and the historical time is relative to the current time, and at a time before the current time, correspondingly, the current traffic is the number of user accesses of the file to be deployed at the current time, and the historical traffic is the number of user accesses of the file to be deployed at the historical time before the current time.
Step S11: calculating the flow change trend from the historical flow to the current flow, wherein the flow change trend is determined according to the flow difference value of the flows at adjacent moments;
it should be noted that the flow rate variation trend in this step is a flow rate difference value at an adjacent time in a period, that is, a value generated by performing a difference operation between the flow rate at a certain time in a period and the flow rate at the previous time, and the flow rate variation trend can be obtained according to the magnitude and sign of the flow rate difference value. For example, in the flow rate rising stage, the flow rate at the next moment is greater than the flow rate at the previous moment, and the sign of the flow rate difference value is positive; in the flow rate decreasing stage, the flow rate at the latter moment is smaller than that at the former moment, and the sign of the flow rate difference value is negative. When the flow rate of a period is embodied as a change curve, when the flow rate is increased, the curve is increased in a positive direction; when the flow rate increase speed is increased, the positive increase amplitude of the curve is increased (the curve is steep); when the flow rate decreases, the curve increases negatively; when the flow rate decreasing speed is increased, the negative amplification degree of the curve is increased.
Further, the flow rate variation tendency may include a rate of change between flow rates at a plurality of adjacent times, that is, a tangent value calculated by deriving a curve at an arbitrary time in a flow rate variation curve having a flow rate value on the ordinate and a time value on the abscissa. For example, the flow rate difference values at 3 consecutive moments are taken, and when the flow rate difference values are positive numbers, the flow rate is in an increasing stage; if the 3 flow rate differences are positive and gradually increase, it means that the acceleration rising stage is in the process. For example, the flow rate difference at 3 consecutive times is taken, and if the flow rate difference is negative, the flow rate is in the descending stage, and if the flow rate difference is negative and the absolute value is gradually increased, the flow rate difference means that the flow rate is in the acceleration descending stage at this time. For another example, the flow difference values at 3 consecutive moments are taken, and when the flow difference values are all 0, the flow is in a stationary period; when the flow difference value contains 0, a positive value and a negative value, the flow is in the fluctuation period. Because the user access number of the file to be deployed at the historical moment and the current moment may change, for example, in the process of developing from the historical moment to the current moment, the user access number of the file to be deployed is continuously reduced or continuously increased, which are the flow change trends of the historical flow value and the current flow, the flow change trend in the step can reflect the heat change situation of the file to be deployed in a historical period from the historical moment to one end of the current moment, and further can reflect the heat trend of the file to be deployed at a future moment after the current moment. It should be noted that the flow rate variation trend in this step can be obtained by calculating the tangential slopes of the curve at the historical time and the current time in the graph from the historical flow rate to the current flow rate.
Step S12: and generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient.
The method comprises the steps of correspondingly adjusting an initial redundancy coefficient based on a flow change trend so as to generate a heat development trend suitable for a file to be deployed, deploying a new redundancy coefficient for the file to be deployed at the current moment, and adjusting the deployment number of the file to be deployed at the current moment through the new redundancy coefficient, so that the deployment number of the file to be deployed can be adapted to the access requirement of a user on the file to be deployed at the future moment. The initial redundancy coefficient in this step represents a redundancy coefficient used when the file to be deployed is deployed at a time before the current time, and may be a coefficient value set by a user according to actual experience, or may be generated based on a flow rate change trend at a historical time at a time before the current time.
As a preferred embodiment, the initial redundancy coefficient may be 1, and when the initial redundancy coefficient is 1, the number of deployments in file deployment is not affected, so that the generation of the new redundancy coefficient at the current time is performed by using 1 as the value of the initial redundancy coefficient, which has higher accuracy.
According to the node data deployment method provided by the invention, the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment are firstly obtained, the flow change trend of the historical flow changing to the current flow is further calculated, and a new redundancy coefficient used for deploying the file to be deployed is further generated based on the flow change trend and the initial redundancy coefficient. The redundancy coefficient is generated according to the flow change trend of the file to be deployed between the historical time and the current time, so that the method can adapt to the scene that the flow of the data file is changed at any time and is relatively variable due to the fact that the access requirement of the current user terminal on the data file is relatively variable, the situation that the deployment of the file to be deployed occupies too many service node resources or cannot meet the access requirement of the user terminal can be relatively avoided, and the overall accuracy of the deployment quantity of the file to be deployed is relatively ensured.
Fig. 2 is a flowchart of another node data deployment method according to an embodiment of the present invention. Referring to fig. 2, the specific steps of the node data deployment method include:
step S20: the method comprises the steps of obtaining the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment.
Step S21: and calculating the flow change trend from the historical flow to the current flow, wherein the flow change trend is determined according to the flow difference value of the flows at adjacent moments.
Step S22: and when the flow variation trend is an increasing trend, the initial redundancy coefficient is adjusted upwards to generate a new redundancy coefficient.
It can be understood that when the trend of the flow changes to be a growing trend, the trend of the heat development of the file to be deployed at a future moment is indicated to be an increasing trend, and therefore in this case, the initial redundancy coefficient is adjusted up to generate a new redundancy coefficient.
Step S23: and when the flow variation trend is a descending trend, the initial redundancy coefficient is adjusted downwards to generate a new redundancy coefficient.
It can be understood that when the flow rate variation trend is a downward trend, the trend of the heat development of the file to be deployed at a future time is indicated to be a downward trend, and therefore in this case, the initial redundancy coefficient is adjusted downward to generate a new redundancy coefficient.
Step S24: and when the flow variation trend is a stable trend, setting the initial redundancy coefficient as a new redundancy coefficient, and deploying the file to be deployed based on the new redundancy coefficient.
It can be understood that when the flow rate variation trend is a steady trend, it indicates that the heat of the file to be deployed at a future time still keeps a steady trend, so that the initial redundancy coefficient is used, that is, the initial redundancy coefficient is set as a new redundancy coefficient.
In the embodiment, the rising, the falling and the stability of the flow change trend are taken as the corresponding change trend of the file to be deployed in the future flow, so that the redundancy coefficient is improved under the condition that the flow change trend rises, the redundancy coefficient is reduced under the condition that the flow change trend falls, and the redundancy coefficient is kept under the condition that the flow change trend is stable, thereby further ensuring the real-time performance of the change of the redundancy coefficient and ensuring the overall accuracy of the deployment quantity when the file to be deployed is deployed.
On the basis of the above embodiment, as a preferred implementation, when the flow rate variation trend is an increasing trend, the up-adjusting the initial redundancy coefficient to generate a new redundancy coefficient includes:
when the flow variation trend is a growth trend, the amplitude of the growth trend is used as an ascending amplitude weight to adjust the initial redundancy coefficient up to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a descending trend, the initial redundancy coefficient is adjusted downwards to generate a new redundancy coefficient, and the method comprises the following steps:
and when the flow variation trend is a descending trend, taking the amplitude of the descending trend as a descending amplitude weight value to lower the initial redundancy coefficient to generate a new redundancy coefficient.
It should be noted that, because both the increasing trend and the decreasing trend in the flow rate variation trend have corresponding increasing amplitude and decreasing amplitude, and the increasing amplitude and the decreasing amplitude can more accurately reflect the degree of increase and the degree of decrease of the heat of the file to be deployed, the increasing amplitude and the decreasing amplitude can be further used as a prediction basis for the degree of change of the heat of the file to be deployed at a future moment according to the specific amplitude of the flow rate variation trend, and further, when the flow rate variation trend is the increasing trend, the increasing amplitude is used as an increasing amplitude weight to raise an initial redundancy coefficient to generate a new redundancy coefficient; and when the flow change trend is a descending trend, the amplitude of the descending trend is used as a descending amplitude weight value to lower the initial redundancy coefficient to generate a new redundancy coefficient, so that the accuracy of the change of the redundancy coefficient is further ensured, and the overall accuracy of the deployment quantity when the file to be deployed is ensured.
On the basis of the above embodiment, as a preferable embodiment, the number of the history times is greater than or equal to 2;
correspondingly, when the flow variation trend is a growth trend, the amplitude of the growth trend is used as an ascending amplitude weight to adjust up the initial redundancy coefficient to generate a new redundancy coefficient, which comprises the following steps:
when the flow variation trend is an accelerated growth trend, the amplitude of the growth trend is used as an ascending amplitude weight to adjust the initial redundancy coefficient upwards to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a descending trend, the amplitude of the descending trend is used as a descending amplitude weight value to lower the initial redundancy coefficient to generate a new redundancy coefficient, and the method comprises the following steps:
when the flow variation trend is an accelerated descending trend, the amplitude of the descending trend is used as a descending amplitude weight to lower the initial redundancy coefficient to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a steady trend, the initial redundancy coefficient is set as a new redundancy coefficient, and the method comprises the following steps:
and when the overall variation amplitude of the flow rate variation trend is 0 or when both the ascending trend and the descending trend of the flow rate variation trend exist, setting the initial redundancy coefficient as a new redundancy coefficient.
It should be noted that, in order to further ensure accurate generation of the new redundancy coefficient, the number of the selected historical time is greater than or equal to 2, that is, the traffic change trend including at least two time periods is counted together with the current time, and the traffic change trend of the historical period of the file to be deployed before the current time can be more accurately obtained. Under the condition, when the flow change trend is an accelerated growth trend, namely the flow growth amplitude in the time period from the historical moment to the current moment is continuously increased, the amplitude of the growth trend is used as an increasing amplitude weight to adjust the initial redundancy coefficient up to generate a new redundancy coefficient; when the flow change trend is an accelerated descending trend, namely the flow reduction amplitude in a time period between the historical moment and the current moment is continuously increased, the amplitude of the descending trend is used as a descending amplitude weight value to lower the initial redundancy coefficient to generate a new redundancy coefficient; and when the overall variation amplitude of the flow rate variation trend is 0 or when both the ascending trend and the descending trend of the flow rate variation trend exist, setting the initial redundancy coefficient as a new redundancy coefficient. It should be further noted that, in a practical application scenario, the flow rate variation trend in a steady state is usually floating up and down and is not prone to severe floating, so when both the upward trend and the downward trend of the flow rate variation trend exist, the flow rate variation trend is also considered to be in a steady state. According to the method and the device, the flow change trend of the file to be deployed in the historical period before the current moment can be more accurately obtained, the accuracy of the change of the redundancy coefficient is further ensured, and the overall accuracy of the deployment quantity of the file to be deployed is ensured when the file to be deployed is deployed.
Further, on the basis of the above-described embodiment, as a preferred embodiment, when the overall variation width of the flow rate variation trend is 0 or both of an upward trend and a downward trend in the flow rate variation trend exist, the setting of the initial redundancy coefficient as the new redundancy coefficient includes:
and when the overall variation amplitude of the flow variation trend is 0 or when both the ascending trend and the descending trend of the flow variation trend exist and do not exceed the preset fluctuation amplitude, setting the initial redundancy coefficient as a new redundancy coefficient.
It should be noted that, in order to further ensure the accuracy of determining that the flow rate variation trend is in a steady trend, the present embodiment introduces the preset fluctuation range, which is the maximum range of the flow rate variation trend in the steady trend in the aspects of the rising trend and the falling trend, where the preset fluctuation range is determined according to the actual user access characteristics, and when the difference of files accessed by the user in a short time is large, the preset fluctuation range may be relatively large, and conversely, when the difference of files accessed by the user in a short time is small, the preset fluctuation range may be relatively small. The method and the device further ensure the accuracy of the change of the redundancy coefficient and the overall accuracy of the deployment quantity when the files to be deployed are deployed.
In the above, the embodiment of the node data deployment method is described in detail, and the present invention further provides a node data deployment apparatus corresponding to the method.
Fig. 3 is a structural diagram of a node data deployment apparatus according to an embodiment of the present invention.
Referring to fig. 3, a node data deployment apparatus 1 according to an embodiment of the present invention includes a memory 11, a processor 12, and a bus 13, where the memory 11 stores a node data deployment program that can be transmitted to the processor 12 through the bus 13 and run on the processor 12, and when the node data deployment program is executed by the processor 12, the node data deployment method is implemented.
The node data deployment apparatus 1 may be a node constituting a CDN network or a blockchain network. May be nodes that make up a CDN network or a blockchain network.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the node data deployment apparatus 1, for example a hard disk of the node data deployment apparatus 1. The memory 11 may also be an external storage device of the node data deployment apparatus 1 in other embodiments, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the node data deployment apparatus 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the node data deployment apparatus 1. The memory 11 may be used not only to store application software installed in the node data deployment apparatus 1 and various types of data, such as codes of a video transcoding program, but also to temporarily store data that has been output or is to be output.
The processor 12 may be, in some embodiments, a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip for executing program codes stored in the memory 11 or Processing data, such as executing a video transcoding program.
The bus 13 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 3, but this does not mean only one bus or one type of bus.
The node data deployment device provided by the invention firstly obtains the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment, further calculates the flow change trend of the historical flow changing to the current flow, and further generates a new redundancy coefficient used for deploying the file to be deployed based on the flow change trend and the initial redundancy coefficient. The device generates the redundancy coefficient according to the flow change trend of the file to be deployed between the historical time and the current time, so that the device can adapt to the scene that the flow of the data file is changed at any time and is relatively variable due to the fact that the access demand of the current user terminal to the data file is relatively variable, the situation that the deployment of the file to be deployed occupies too many service node resources or cannot meet the access demand of the user terminal can be relatively avoided, and the overall accuracy of the deployment quantity when the file to be deployed is relatively ensured.
In addition, the invention also provides a node data deployment system, which comprises:
the flow acquiring unit is used for acquiring the current flow of the file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment;
the trend calculation unit is used for calculating the flow change trend from the historical flow to the current flow, and the flow change trend is determined according to the flow difference value of the flows at adjacent moments;
and the redundancy coefficient generation module is used for generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient.
The node data deployment system provided by the invention firstly obtains the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment, further calculates the flow change trend of the historical flow changing to the current flow, and further generates a new redundancy coefficient used for deploying the file to be deployed based on the flow change trend and the initial redundancy coefficient. The system is a redundancy coefficient generated according to the flow change trend of the file to be deployed between the historical time and the current time, so that the system can adapt to the scene that the flow of the data file is changed at any time and is relatively variable due to the fact that the access demand of the current user terminal to the data file is relatively variable, the situation that the deployment of the file to be deployed occupies too many service node resources or cannot meet the access demand of the user terminal can be relatively avoided, and the overall accuracy of the deployment quantity when the file to be deployed is relatively ensured.
Furthermore, the present invention also provides a computer-readable storage medium, on which a redundancy coefficient generation program is stored, where the redundancy coefficient generation program can be executed by one or more processors to implement the node data deployment method as described above.
The computer-readable storage medium provided by the invention firstly acquires the current flow of the file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment, further calculates the flow change trend of the historical flow changing to the current flow, and further generates a new redundancy coefficient used for deploying the file to be deployed based on the flow change trend and the initial redundancy coefficient. The computer-readable storage medium is a redundancy coefficient generated according to the flow change trend of the file to be deployed between the historical time and the current time, so that the computer-readable storage medium can adapt to the scene that the flow of the data file is changed and changed relatively along with the change of the time due to the fact that the access demand of the current user terminal on the data file is relatively variable, the situation that the deployment of the file to be deployed occupies too much service node resources or cannot meet the access demand of the user terminal can be relatively avoided, and the overall accuracy of the deployment quantity of the file to be deployed is relatively ensured.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A node data deployment method is characterized by comprising the following steps:
acquiring the current flow of a file to be deployed at the current moment and the historical flow of a file to be deployed at a historical moment before the current moment;
calculating a flow change trend from the historical flow to the current flow, wherein the flow change trend is determined according to a flow difference value of flows at adjacent moments;
and generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient.
2. The node data deployment method according to claim 1, wherein the generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the traffic variation trend includes:
when the flow variation trend is an increasing trend, the initial redundancy coefficient is adjusted upwards to generate a new redundancy coefficient;
when the flow variation trend is a descending trend, the initial redundancy coefficient is adjusted downwards to generate a new redundancy coefficient;
and when the flow variation trend is a steady trend, setting the initial redundancy coefficient as the new redundancy coefficient.
3. The node data deployment method according to claim 2, wherein the generating the new redundancy coefficient by up-regulating the initial redundancy coefficient when the traffic variation trend is a growth trend comprises:
when the flow variation trend is a growth trend, taking the amplitude of the growth trend as an ascending amplitude weight to adjust the initial redundancy coefficient upwards to generate a new redundancy coefficient;
correspondingly, when the flow rate variation trend is a descending trend, the step of reducing the initial redundancy coefficient to generate the new redundancy coefficient includes:
and when the flow variation trend is a descending trend, taking the amplitude of the descending trend as a descending amplitude weight value to lower the initial redundancy coefficient to generate the new redundancy coefficient.
4. The node data deployment method of claim 3, wherein the number of historical time instants is greater than or equal to 2;
correspondingly, when the flow rate variation trend is a growth trend, taking the amplitude of the growth trend as an ascending amplitude weight to adjust up the initial redundancy coefficient to generate the new redundancy coefficient, including:
when the flow variation trend is an accelerated growth trend, taking the amplitude of the growth trend as an ascending amplitude weight to adjust the initial redundancy coefficient upwards to generate the new redundancy coefficient;
correspondingly, when the flow rate variation trend is a descending trend, the step of taking the amplitude of the descending trend as a descending amplitude weight value to lower the initial redundancy coefficient to generate the new redundancy coefficient includes:
when the flow variation trend is an accelerated descending trend, the amplitude of the descending trend is used as a descending amplitude weight value to lower the initial redundancy coefficient to generate the new redundancy coefficient;
correspondingly, when the flow rate variation trend is a steady trend, the setting the initial redundancy coefficient as the new redundancy coefficient includes:
and when the overall variation amplitude of the flow variation trend is 0 or when both an ascending trend and a descending trend of the flow variation trend exist, setting the initial redundancy coefficient as the new redundancy coefficient.
5. The node data deployment method according to claim 4, wherein the setting the initial redundancy coefficient to the new redundancy coefficient when the overall variation amplitude of the traffic variation trend is 0 or when both an upward trend and a downward trend of the traffic variation trend exist comprises:
and when the overall variation amplitude of the flow variation trend is 0 or both the ascending trend and the descending trend exist in the flow variation trend and do not exceed the preset fluctuation amplitude, setting the initial redundancy coefficient as the new redundancy coefficient.
6. The node data deployment method according to any one of claims 1 to 5, wherein the initial redundancy coefficient is 1.
7. A node data deployment device, characterized in that the device comprises a memory, a processor and a bus, wherein the memory stores a redundancy coefficient generation program which can be transmitted to the processor by the bus and run on the processor, and the redundancy coefficient generation program realizes the node data deployment method according to any one of claims 1 to 6 when executed by the processor.
8. The apparatus of claim 7, wherein the apparatus is a node constituting a CDN network or a blockchain network.
9. A node data deployment system, the system comprising:
the device comprises a flow acquiring unit, a flow acquiring unit and a flow acquiring unit, wherein the flow acquiring unit is used for acquiring the current flow of a file to be deployed at the current moment and the historical flow of the file to be deployed at the historical moment before the current moment;
the trend calculation unit is used for calculating a flow change trend from the historical flow to the current flow, and the flow change trend comprises a flow difference value of flows at adjacent moments;
and the redundancy coefficient generation module is used for generating a new redundancy coefficient for deploying the file to be deployed based on the initial redundancy coefficient and the flow change trend, and deploying the file to be deployed based on the new redundancy coefficient.
10. A computer-readable storage medium having stored thereon a redundancy coefficient generation program executable by one or more processors to implement the node data deployment method according to any one of claims 1 to 6.
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