CN117472589B - Park network service management method and system - Google Patents
Park network service management method and system Download PDFInfo
- Publication number
- CN117472589B CN117472589B CN202311810659.9A CN202311810659A CN117472589B CN 117472589 B CN117472589 B CN 117472589B CN 202311810659 A CN202311810659 A CN 202311810659A CN 117472589 B CN117472589 B CN 117472589B
- Authority
- CN
- China
- Prior art keywords
- data
- utilization rate
- cpu utilization
- data sequence
- server
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 45
- 238000004364 calculation method Methods 0.000 claims abstract description 29
- 230000005540 biological transmission Effects 0.000 claims description 45
- 238000000034 method Methods 0.000 claims description 36
- 230000001965 increasing effect Effects 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 5
- 230000003287 optical effect Effects 0.000 claims description 5
- 238000005192 partition Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 6
- 230000008859 change Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention relates to the technical field of data processing, in particular to a park network service management method and system, comprising the following steps: acquiring a CPU utilization rate data sequence of a reference server, acquiring a growing trend value of each data in the CPU utilization rate data sequence, sequentially obtaining a predicted used resource amount, an allocable calculation resource amount and an actual allocation resource amount of the CPU utilization rate data sequence, acquiring processed data and unprocessed data, transmitting all the processed data and unprocessed data to a park total terminal server, and performing operation processing on all the unprocessed data by using the park total terminal server to obtain processing results of all the unprocessed data. According to the invention, the computing resources on each server are reasonably used by utilizing the corresponding actual allocation resource quantity of each server, so that the data operation processing efficiency of the campus network server is improved, and the effect of the campus network service management is enhanced.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a park network service management method and system.
Background
The intelligent park is by means of information technologies such as cloud computing, internet of things, analysis optimization and the like of a new generation, the existing information technologies such as Internet, sensors and intelligent information processing are highly integrated, and scattered physical infrastructure, information infrastructure, social infrastructure and business infrastructure in the park are connected in a monitoring, analysis, integration and intelligent response mode, so that accuracy, high efficiency and flexibility of services are improved, operation cost of enterprises is reduced, a novel park of an autonomous innovation service system is established, and the goals of sustainable development of park economy and improvement of an industrial value chain are achieved.
The existing problems are as follows: at present, in a high-integration multi-channel information collection mode of a park, huge data can be obtained, if all the data are uploaded to core computing equipment for park network service management and are calculated, huge network load conditions can be generated, even flow congestion is caused, and the whole park network service management system has a breakdown condition, so that the effect of park network service management is reduced.
Disclosure of Invention
The invention provides a park network service management method and system, which are used for solving the existing problems.
The invention relates to a park network service management method and a system, which adopt the following technical scheme:
one embodiment of the present invention provides a method for managing a campus network service, the method comprising the steps of:
recording any one park network server as a reference server; collecting CPU utilization rate of a reference server in any period of time to obtain a CPU utilization rate data sequence; in a CPU utilization rate data sequence, performing data fitting on all data by using a high-order polynomial model to obtain fitting data of each data; obtaining a growth trend value of each data according to fitting data of each data in the CPU utilization rate data sequence;
obtaining predicted used resource quantity of the CPU utilization rate data sequence according to fitting data and an increasing trend value of all data in the CPU utilization rate data sequence;
obtaining the allocable computing resource quantity of the CPU utilization rate data sequence according to the predicted utilization resource quantity of the CPU utilization rate data sequence;
obtaining the actual allocation resource quantity of the CPU utilization rate data sequence according to the allocable calculation resource quantity of the CPU utilization rate data sequence;
recording all data acquired by the reference server in any period of time as data to be processed; obtaining processed data and unprocessed data of all data partitions to be processed according to the actual allocation resource quantity of the CPU utilization rate data sequence; and transmitting all the processed data and the unprocessed data to a park total terminal server, and performing operation processing on all the unprocessed data by using the park total terminal server to obtain processing results of all the unprocessed data.
Further, the fitting data of each data in the CPU usage rate data sequence is used for obtaining the growth trend value of each data, and the method comprises the following specific steps:
in fitting data of all data in the CPU utilization rate data sequence, a first derivative method is used for obtaining a first derivative of fitting data of each data in the CPU utilization rate data sequence;
recording any one data in the CPU utilization rate data sequence as target data;
obtaining the growth trend parameter of the target data according to the first derivative of the fitting data of the target data and the ordinal value of the target data in the CPU utilization rate data sequence;
and obtaining the growth trend value of the target data according to the growth trend parameter of the target data and the first derivative of the fitting data of the target data.
Further, the specific calculation formula corresponding to the growth trend parameter of the target data is obtained according to the first derivative of the fitting data of the target data and the ordinal value of the target data in the CPU usage rate data sequence, wherein the specific calculation formula comprises:
wherein K is an increasing trend parameter of the target data, t is an ordinal value of the target data in the CPU usage data sequence,first derivative of fitting data for target data, +.>Is a logarithmic function based on natural constants, and is an absolute function.
Further, the specific calculation formula corresponding to the growth trend value of the target data is obtained according to the growth trend parameter of the target data and the first derivative of the fitting data of the target data, wherein the specific calculation formula is as follows:
where G is the increasing trend value of the target data, K is the increasing trend parameter of the target data,first derivative of fitting data for target data, +.>Is a linear normalization function.
Further, the specific calculation formula corresponding to the predicted used resource amount of the CPU usage rate data sequence is obtained according to the fitting data and the growth trend value of all the data in the CPU usage rate data sequence:
where Y is the predicted amount of resources used by the CPU usage data sequence,for the increasing trend value of the ith data in the CPU usage data sequence,/for the CPU usage data sequence>Fitting data of ith data in the CPU utilization rate data sequence, wherein n is the data quantity in the CPU utilization rate data sequence,/for the data>Is->Differential of->To->For integrating variable pairs->Integrating->Is->All->Is the maximum value of (a).
Further, the method for obtaining the allocable computing resource amount of the CPU utilization rate data sequence according to the predicted utilization resource amount of the CPU utilization rate data sequence comprises the following specific steps:
obtaining the total cache amount of the CPU of the reference server according to the model and specification of the CPU of the reference server;
calculating the product of the total CPU buffer memory amount of the reference server and a preset constant, and subtracting the difference value of the predicted used resource amount of the CPU utilization rate data sequence from the product to be recorded as the allocable calculated resource amount of the CPU utilization rate data sequence.
Further, the obtaining the actual allocation resource amount of the CPU utilization data sequence according to the allocable calculation resource amount of the CPU utilization data sequence includes the following specific steps:
using a server monitoring tool to obtain the bandwidth utilization rate of a reference server;
obtaining the transmission rate of a reference server by using a network performance testing tool;
obtaining the channel length of a reference server by using an optical power meter;
obtaining the transmission influence quantity of the reference server according to the bandwidth utilization rate, the transmission rate and the channel length of the reference server;
and recording the product of the transmission influence quantity of the reference server and the allocatable calculation resource quantity of the CPU utilization rate data sequence as the actual allocation resource quantity of the CPU utilization rate data sequence.
Further, the obtaining the transmission influence quantity of the reference server according to the bandwidth utilization rate, the transmission rate and the channel length of the reference server includes the following specific steps:
and calculating the product of the transmission rate and the bandwidth utilization rate of the reference server, calculating the quotient of the product of the transmission rate and the bandwidth utilization rate divided by the channel length of the reference server, and recording the normalized value of the quotient as the transmission influence quantity of the reference server.
Further, the method for obtaining the processed data and the unprocessed data of all the data partitions to be processed according to the actual allocation resource amount of the CPU utilization rate data sequence comprises the following specific steps:
in any period of time, according to the actual allocation resource quantity of the CPU utilization rate data sequence, sequentially performing operation processing on all data to be processed by using a reference server to obtain a processing result of each data to be processed;
at the last moment in any period of time, recording the data to be processed, which have obtained the processing result, as processed data; and marking the data to be processed which does not obtain the processing result as unprocessed data.
The invention also provides a park network service management system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program stored in the memory to realize the steps of the park network service management method.
The technical scheme of the invention has the beneficial effects that:
in the embodiment of the invention, the CPU utilization rate of the reference server is acquired within any period of time to obtain a CPU utilization rate data sequence, and the growth trend value of each data in the CPU utilization rate data sequence is acquired, so that the predicted utilization resource quantity, the allocable calculation resource quantity and the actual allocation resource quantity of the CPU utilization rate data sequence are sequentially obtained. The method acquires available computing resources of the server, namely the actual allocation resource amount, through CPU cache information and CPU utilization rate, so that data operation processing of the server is carried out, and the operation efficiency of the server is guaranteed. And recording all data acquired by the reference server in any period of time as data to be processed, obtaining processed data and unprocessed data divided by all data to be processed according to the actual allocation resource amount, transmitting all processed data and unprocessed data to a park total terminal server, and performing operation processing on all unprocessed data by using the park total terminal server to obtain processing results of all unprocessed data, thereby reducing the operation pressure of the park total terminal server through the data operation processing of each server, and further improving the operation efficiency of all servers in the park. The invention reasonably uses the computing resources on each server by utilizing the corresponding actual allocation resource quantity of each server, and improves the data operation processing efficiency of the campus network server, thereby enhancing the effect of the management of the campus network service.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for managing a campus network service according to the present invention;
fig. 2 is a schematic diagram of a campus network node server distribution according to the present embodiment.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific implementation, structure, characteristics and effects of a method and a system for managing a campus network service according to the present invention, which are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a method and a system for managing a park network service provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart illustrating steps of a method for managing a campus network service according to an embodiment of the present invention is shown, the method includes the following steps:
step S001: recording any one park network server as a reference server; collecting CPU utilization rate of a reference server in any period of time to obtain a CPU utilization rate data sequence; in a CPU utilization rate data sequence, performing data fitting on all data by using a high-order polynomial model to obtain fitting data of each data; and obtaining the growth trend value of each data according to the fitting data of each data in the CPU utilization rate data sequence.
The main purpose of this embodiment is to allocate computing resources based on the devices of the campus, so as to achieve overall allocation of computing resources of the whole campus without modifying the network nodes of the campus, instead of centralizing all computing pressures on certain given edge nodes, thereby wasting idle computing resources of common nodes. When the data is calculated and integrated, the calculation resource of each node on the data transmission path is reasonably utilized, so that the data is processed more rapidly and effectively, and the service efficiency of the whole park network service system is enhanced.
Fig. 2 is a schematic diagram of a campus network node server distribution according to the present embodiment.
What needs to be described is: all servers in fig. 2, which are terminal nodes, transmit the collected data to the segment servers in fig. 2, which are child nodes, respectively, and the segment servers in fig. 2 are segment servers 1, 2, 3, 4, and 5, respectively. All the patch servers then transmit the collected data to the campus master terminal server, which is the root node. In fig. 2, the direction of the connection line between the servers indicates the data flow direction, and the length indicates the distance of transmission. In the data acquisition process, the transmission distance between the nodes in fig. 2 and the data obtained by the nodes within the node range are acquired, and in addition, the resource use condition of the nodes in a period of time and the transmission condition of the data between the nodes are also required to be acquired, namely, the related information of the network node server of the park is obtained, so that the resource allocation is convenient to follow-up. The resource usage conditions, such as CPU usage, memory usage, network traffic, etc., and the transmission conditions, such as network load, transmission rate, etc.
Any one of the campus network servers is denoted as a reference server. And collecting CPU utilization rate of the reference server within any period of time to obtain a CPU utilization rate data sequence.
What needs to be described is: any one of the campus network servers is the server in fig. 2 or the patch server, and cannot be the total terminal server of the campus. The CPU utilization rate can reflect the resource utilization condition of the server, and the Chinese full name of the CPU is a central processing unit, and the English full name of the CPU is Central Processing Unit. In this embodiment, the data transmission is performed by the server every two hours, and the acquisition frequency of the CPU usage is set to be once every 3 seconds, which is described as an example, and other values may be set in other embodiments, which is not limited in this embodiment. Thus the arbitrary period of time is two hours prior to the server data transmission.
Therefore, the CPU utilization rate data sequence can be analyzed and predicted to obtain the predicted resource utilization amount in the next period of time.
And using a high-order polynomial model to perform data fitting on the data in the CPU utilization rate data sequence to obtain fitting data of each data in the CPU utilization rate data sequence.
And in the fitting data of all the data in the CPU utilization rate data sequence, a first derivative method is used to obtain the first derivative of the fitting data of each data in the CPU utilization rate data sequence.
The higher order polynomial model and the first derivative method are known techniques, and the specific method is not described here.
Any one data in the CPU usage rate data sequence is recorded as target data.
The calculation formula of the growth trend value G of the target data is known as follows:
wherein G is the increasing trend value of the target data, K is the increasing trend parameter of the target data, t is the ordinal number value of the target data in the CPU utilization rate data sequence,first derivative of fitting data for target data, +.>As a logarithmic function based on natural constants, < ->Normalizing the data values to [0,1 ] as a linear normalization function]Within the interval. I is an absolute function.
What needs to be described is: the data change in the CPU utilization rate data sequence has incremental and decremental fluctuation change, so that polynomial is first used to obtain one clear data change fluctuation curve, and thus the growth trend parameter may be obtained based on the data change trend, and the CPU utilization rate is known to be 0-100% and thus the CPU utilization rate is known to be 0-100%The value of (2) is in the range of-1 to 1, when +.>Positive, and the larger the data trend is, at this time +.>Along with->Is increased by an increase of>Negative and the smaller the trend of data decrease is, the greater is the +.>Along with->Is increased by decreasing, so->The larger the data trend is, wherein +.>To prevent the denominator from being 0. In the process of acquiring data by the known server, the acquired data is firstly subjected to operation processing, and then the acquired data is transmitted to the next server without operation, so that the earlier the acquired data is, the more important the change trend of the acquired data is, and therefore the data is used>A growing trend parameter representing the target data, when t is smaller, and +.>The larger the K, the larger the K. Thus, the increasing trend of the use amount of K log resources is utilized to adjust when->I.e./>In order to be positive in this respect,the larger the trend, the larger the trend change, thus use +.>Represents the increasing trend value of the target data when +.>I.e.When it is negative, it is added>The smaller the trend, the greater the trend change, thus use +.>Representing the increasing trend value of the target data.
According to the mode, the increasing trend value of each data in the CPU utilization rate data sequence is obtained.
Step S002: and obtaining the predicted used resource amount of the CPU utilization rate data sequence according to the fitting data and the growth trend value of all the data in the CPU utilization rate data sequence.
Therefore, the subsequent resource use condition can be predicted by utilizing the growing trend, and the calculation formula of the predicted use resource amount Y of the CPU use rate data sequence is as follows:
where Y is the predicted amount of resources used by the CPU usage data sequence,for the increasing trend value of the ith data in the CPU usage data sequence,/for the CPU usage data sequence>Fitting data of ith data in the CPU utilization rate data sequence, wherein n is the data quantity in the CPU utilization rate data sequence,/for the data>Is->Differential of->To->For integrating variable pairs->Integrating->Is->All->Is the maximum value of (a).
What needs to be described is: both differentiation and integration are fundamental operations in mathematics, which are well known techniques, and integration is used to solve the cumulative total or rate of change of a function over a certain interval. Is known to bePolynomial fitting function corresponding to data in the CPU usage data sequence,/for>Is according to->And (5) obtaining. When the amount of allocatable computing resources is predicted, it is necessary to reserve as much computing resources as possible without impeding the normal operation of the original server, so that the trend of increase is used to estimate the amount of resources usedTrending the function, then taking the maximum resource usage in the data retention time, i.eThe method takes the method as a reference to predict the used resource quantity, can ensure that the original server operation is not influenced, thereby obtaining the predicted used resource quantity Y of the CPU utilization rate data sequence, and the Y also represents the maximum CPU buffer storage quantity in the predicted practical application process after fitting and analyzing according to the data.
Step S003: and obtaining the allocatable computing resource quantity of the CPU utilization rate data sequence according to the predicted utilization resource quantity of the CPU utilization rate data sequence.
It is known that detailed information about the CPU cache can be found by looking up the CPU model and specification of the server. Therefore, the embodiment obtains the total CPU buffer capacity of the reference server according to the CPU model and specification of the reference server.
Thus, the amount of the assignable computing resources of the CPU utilization data sequence can be knownThe calculation formula of (2) is as follows:
wherein the method comprises the steps ofAn amount of allocatable computing resources for the CPU utilization data sequence, Y is an amount of predicted utilization resources for the CPU utilization data sequence,/for the CPU utilization data sequence>For the total cache amount of the CPU of the reference server, < >>For a preset constant, the present embodiment sets +.>80%, by way of example, in other embodimentsOther values are set, and the present embodiment is not limited.
What needs to be described is: CPU utilization typically needs to be left at 20% in order to respond quickly when needed. Y will therefore be less than 80% of the total CPU cache, thereby usingAn amount of allocatable computing resources representing a CPU usage data sequence.
Step S004: and obtaining the actual allocation resource quantity of the CPU utilization rate data sequence according to the allocable calculation resource quantity of the CPU utilization rate data sequence.
And obtaining the bandwidth utilization rate of the reference server by using a server monitoring tool. And obtaining the transmission rate of the reference server by using a network performance testing tool. The channel length of the reference server is obtained using an optical power meter.
What needs to be described is: the server monitoring tool, the network performance testing tool and the optical power meter are all known technologies, and specific methods are not described herein. The bandwidth utilization rate refers to the ratio of the bandwidth used by the network interface of the server to the total bandwidth, and the bandwidth utilization rate of the server can be obtained by using a server monitoring tool or a network traffic statistics tool, and can also be obtained by using a system command or a script. The transmission rate refers to the speed at which data is transmitted in the network, and is typically measured in units of data transmitted per second, and obtaining the transmission rate of the server requires the use of network performance testing tools. Channel length refers to the distance an electrical signal travels in a transmission medium and obtaining the channel length of a server requires the use of specialized test equipment, such as an optical power meter.
The calculation formula of the transmission influence quantity C of the reference server is known as follows:
wherein C is the transmission influence quantity of the reference server, v is the transmission rate of the reference server, B is the bandwidth utilization rate of the reference server, and L is the channel length of the reference server.
What needs to be described is: for the followingFor channel transmission, when the channel load is higher, the channel is busy, and the transmission capacity is smaller, so that the transmission content is smaller and better, and therefore, the more calculation resources are required to be allocated to data for calculation, the larger the transmission influence parameters are. When the channel length L is longer, the transmission efficiency is lower, and in order to ensure the real-time performance of the data, the data needs to be transmitted as soon as possible, so that the calculation amount of allocation is reduced to speed up the transmission time. Similarly, when the transmission rate v is higher, the time taken for transmission is smaller, the more computing resources can be owned, the larger the transmission influence amount is, and when the B is larger, the more available computing resources are indicated, the larger the transmission influence amount is. Thus usingRepresenting the transmission impact of the reference server.
Thereby knowing the actual allocation resource amount of the CPU usage data sequenceThe calculation formula of (2) is as follows:
wherein the method comprises the steps ofActually allocated resource amount for CPU usage data sequence,/-for CPU usage data sequence>And C is the transmission influence quantity of the reference server.
What needs to be described is: the larger C indicates the more computing resources the reference server owns, anThe amount of allocatable computing resources for the CPU usage data sequence, thus +.>Indicating CPU usage numberThe amount of resources is actually allocated according to the sequence.
Step S005: recording all data acquired by the reference server in any period of time as data to be processed; obtaining processed data and unprocessed data of all data partitions to be processed according to the actual allocation resource quantity of the CPU utilization rate data sequence; and transmitting all the processed data and the unprocessed data to a park total terminal server, and performing operation processing on all the unprocessed data by using the park total terminal server to obtain processing results of all the unprocessed data.
And recording all data acquired by the reference server in any period of time as data to be processed.
And in any period of time, according to the actual allocation resource quantity of the CPU utilization rate data sequence, sequentially performing operation processing on all the data to be processed by using a reference server to obtain a processing result of each data to be processed.
And recording the data to be processed, which have obtained the processing result, as processed data at the last moment in any time. And marking the data to be processed which does not obtain the processing result as unprocessed data.
What needs to be described is: the server performs various operations on the received data as known in the art, and the specific method is not described herein. Some common server data processing operations include: data compression, analysis, filtering, cleaning, etc., to produce desired results. The data is required to be transmitted from the server to the zone server and then transmitted to the park total terminal server, so that the data operation processing time of the reference server is the actual allocation resource amount of the CPU utilization rate data sequence for the data operation processing resource available by the reference server in any period of time, which means that the reference server can only process part of the data to be processed to obtain the processing result, and the processed data is marked as processed data, and the unprocessed data is marked as unprocessed data.
And transmitting all the processed data and the unprocessed data to a park total terminal server, and performing operation processing on all the unprocessed data by using the park total terminal server to obtain processing results of all the unprocessed data. Thereby completing the campus network service management.
What needs to be described is: when the reference server is the server in fig. 2, all the processed data and unprocessed data are transmitted to the corresponding tile server, and the actual allocation resource amount of the CPU utilization data sequence of the tile server is obtained in the above manner, so that the operation processing is performed on all the unprocessed data acquired by the tile server, and the processed data in the unprocessed data are obtained. All unprocessed and processed data in the patch server is then transferred to the campus total terminal server. And finally, carrying out operation processing on all unprocessed data acquired by the park total terminal server, transmitting data among all servers, and further comprising a processing result of the processed data. Thus, the operation processing result of all data acquired by all servers in the park can be obtained. Therefore, the data operation amount of the total terminal server in the park is reduced through the data processing of each server.
The present invention has been completed.
In summary, in the embodiment of the present invention, the CPU utilization of the reference server is collected for any period of time to obtain a CPU utilization data sequence, and a growth trend value of each data in the CPU utilization data sequence is obtained, so as to sequentially obtain a predicted usage resource amount, an allocable calculation resource amount, and an actual allocation resource amount of the CPU utilization data sequence. And recording all the data acquired by the reference server in any period of time as data to be processed, obtaining processed data and unprocessed data divided by all the data to be processed according to the actual allocation resource amount, transmitting all the processed data and unprocessed data to a park total terminal server, and performing operation processing on all the unprocessed data by using the park total terminal server to obtain processing results of all the unprocessed data. According to the invention, the computing resources on each server are reasonably used by utilizing the corresponding actual allocation resource quantity of each server, so that the data operation processing efficiency of the campus network server is improved, and the effect of the campus network service management is enhanced.
The invention also provides a park network service management system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program stored in the memory to realize the steps of the park network service management method.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (3)
1. A method of managing a campus network service, the method comprising the steps of:
recording any one park network server as a reference server; collecting CPU utilization rate of a reference server in any period of time to obtain a CPU utilization rate data sequence; in a CPU utilization rate data sequence, performing data fitting on all data by using a high-order polynomial model to obtain fitting data of each data; obtaining a growth trend value of each data according to fitting data of each data in the CPU utilization rate data sequence;
obtaining predicted used resource quantity of the CPU utilization rate data sequence according to fitting data and an increasing trend value of all data in the CPU utilization rate data sequence;
obtaining the allocable computing resource quantity of the CPU utilization rate data sequence according to the predicted utilization resource quantity of the CPU utilization rate data sequence;
obtaining the actual allocation resource quantity of the CPU utilization rate data sequence according to the allocable calculation resource quantity of the CPU utilization rate data sequence;
recording all data acquired by the reference server in any period of time as data to be processed; obtaining processed data and unprocessed data of all data partitions to be processed according to the actual allocation resource quantity of the CPU utilization rate data sequence; transmitting all the processed data and the unprocessed data to a park total terminal server, and performing operation processing on all the unprocessed data by using the park total terminal server to obtain processing results of all the unprocessed data;
the method for obtaining the growth trend value of each data according to the fitting data of each data in the CPU utilization rate data sequence comprises the following specific steps:
in fitting data of all data in the CPU utilization rate data sequence, a first derivative method is used for obtaining a first derivative of fitting data of each data in the CPU utilization rate data sequence;
recording any one data in the CPU utilization rate data sequence as target data;
obtaining the growth trend parameter of the target data according to the first derivative of the fitting data of the target data and the ordinal value of the target data in the CPU utilization rate data sequence;
obtaining a growing trend value of the target data according to the growing trend parameter of the target data and the first derivative of the fitting data of the target data;
the specific calculation formula corresponding to the growth trend parameter of the target data is obtained according to the first derivative of the fitting data of the target data and the ordinal value of the target data in the CPU utilization rate data sequence, wherein the specific calculation formula comprises the following components:
wherein K is an increasing trend parameter of the target data, t is an ordinal value of the target data in the CPU usage data sequence,first derivative of fitting data for target data, +.>Is a logarithmic function with a natural constant as a base, and I is an absolute function;
the specific calculation formula corresponding to the increasing trend value of the target data is obtained according to the increasing trend parameter of the target data and the first derivative of the fitting data of the target data, wherein the specific calculation formula is as follows:
where G is the increasing trend value of the target data, K is the increasing trend parameter of the target data,first derivative of fitting data for target data, +.>Is a linear normalization function;
the specific calculation formula corresponding to the predicted used resource amount of the CPU utilization rate data sequence is obtained according to the fitting data and the growth trend value of all the data in the CPU utilization rate data sequence:
where Y is the predicted amount of resources used by the CPU usage data sequence,for the increasing trend value of the ith data in the CPU usage data sequence,/for the CPU usage data sequence>Fitting data of ith data in the CPU utilization rate data sequence, wherein n is the data quantity in the CPU utilization rate data sequence,/for the data>Is->Differential of->To->For integrating variable pairs->The integration is performed and the integration is performed,is->All->Maximum value of (2);
the method for obtaining the allocatable computing resource quantity of the CPU utilization rate data sequence according to the predicted utilization resource quantity of the CPU utilization rate data sequence comprises the following specific steps:
obtaining the total cache amount of the CPU of the reference server according to the model and specification of the CPU of the reference server;
calculating the product of the total CPU cache amount of the reference server and a preset constant, and subtracting the difference value of the predicted used resource amount of the CPU utilization rate data sequence from the product to be recorded as the allocable calculated resource amount of the CPU utilization rate data sequence;
the method for obtaining the actual allocation resource quantity of the CPU utilization rate data sequence according to the allocable calculation resource quantity of the CPU utilization rate data sequence comprises the following specific steps:
using a server monitoring tool to obtain the bandwidth utilization rate of a reference server;
obtaining the transmission rate of a reference server by using a network performance testing tool;
obtaining the channel length of a reference server by using an optical power meter;
obtaining the transmission influence quantity of the reference server according to the bandwidth utilization rate, the transmission rate and the channel length of the reference server;
the product of the transmission influence quantity of the reference server and the allocatable calculation resource quantity of the CPU utilization rate data sequence is recorded as the actual allocation resource quantity of the CPU utilization rate data sequence;
the method for obtaining the transmission influence quantity of the reference server according to the bandwidth utilization rate, the transmission rate and the channel length of the reference server comprises the following specific steps:
and calculating the product of the transmission rate and the bandwidth utilization rate of the reference server, calculating the quotient of the product of the transmission rate and the bandwidth utilization rate divided by the channel length of the reference server, and recording the normalized value of the quotient as the transmission influence quantity of the reference server.
2. The method for managing the campus network service according to claim 1, wherein the step of obtaining the processed data and the unprocessed data of all the data partitions to be processed according to the actual allocation resource amount of the CPU utilization data sequence comprises the following specific steps:
in any period of time, according to the actual allocation resource quantity of the CPU utilization rate data sequence, sequentially performing operation processing on all data to be processed by using a reference server to obtain a processing result of each data to be processed;
at the last moment in any period of time, recording the data to be processed, which have obtained the processing result, as processed data; and marking the data to be processed which does not obtain the processing result as unprocessed data.
3. A campus network service management system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the steps of a method of campus network service management as claimed in any one of claims 1 to 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311810659.9A CN117472589B (en) | 2023-12-27 | 2023-12-27 | Park network service management method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311810659.9A CN117472589B (en) | 2023-12-27 | 2023-12-27 | Park network service management method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117472589A CN117472589A (en) | 2024-01-30 |
| CN117472589B true CN117472589B (en) | 2024-03-12 |
Family
ID=89633282
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311810659.9A Active CN117472589B (en) | 2023-12-27 | 2023-12-27 | Park network service management method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117472589B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103945548A (en) * | 2014-04-29 | 2014-07-23 | 西安电子科技大学 | Resource distribution system and task/service scheduling method in C-RAN |
| CN111026550A (en) * | 2019-11-29 | 2020-04-17 | 中国科学院深圳先进技术研究院 | Task scheduling method and device under container cloud environment, server and storage device |
| CN111400039A (en) * | 2020-03-10 | 2020-07-10 | 中国联合网络通信集团有限公司 | Control method and device of cloud platform, cloud platform system and electronic equipment |
| CN111488200A (en) * | 2020-06-28 | 2020-08-04 | 四川新网银行股份有限公司 | Virtual machine resource utilization rate analysis method based on dynamic analysis model |
| CN111491006A (en) * | 2020-03-03 | 2020-08-04 | 天津大学 | Load-aware cloud computing resource elastic distribution system and method |
| CN112130997A (en) * | 2020-09-22 | 2020-12-25 | 新华三大数据技术有限公司 | Resource allocation method and device |
| CN112839382A (en) * | 2020-12-30 | 2021-05-25 | 北京邮电大学 | Video semantic-driven communication and computing resource joint allocation method in Internet of vehicles |
| CN113708962A (en) * | 2021-08-20 | 2021-11-26 | 广东工业大学 | Mobile edge computing resource management method based on network slice |
| CN114816750A (en) * | 2022-04-24 | 2022-07-29 | 江苏鼎集智能科技股份有限公司 | Big data management task operation method |
| CN115033377A (en) * | 2022-05-26 | 2022-09-09 | 北京云思智学科技有限公司 | Service resource prediction method and device based on cluster server and electronic equipment |
| CN117114623A (en) * | 2023-09-18 | 2023-11-24 | 广东泰一高新技术发展有限公司 | Intelligent management method and system for monitoring equipment in park |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11824731B2 (en) * | 2021-12-29 | 2023-11-21 | Salesforce, Inc. | Allocation of processing resources to processing nodes |
-
2023
- 2023-12-27 CN CN202311810659.9A patent/CN117472589B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103945548A (en) * | 2014-04-29 | 2014-07-23 | 西安电子科技大学 | Resource distribution system and task/service scheduling method in C-RAN |
| CN111026550A (en) * | 2019-11-29 | 2020-04-17 | 中国科学院深圳先进技术研究院 | Task scheduling method and device under container cloud environment, server and storage device |
| CN111491006A (en) * | 2020-03-03 | 2020-08-04 | 天津大学 | Load-aware cloud computing resource elastic distribution system and method |
| CN111400039A (en) * | 2020-03-10 | 2020-07-10 | 中国联合网络通信集团有限公司 | Control method and device of cloud platform, cloud platform system and electronic equipment |
| CN111488200A (en) * | 2020-06-28 | 2020-08-04 | 四川新网银行股份有限公司 | Virtual machine resource utilization rate analysis method based on dynamic analysis model |
| CN112130997A (en) * | 2020-09-22 | 2020-12-25 | 新华三大数据技术有限公司 | Resource allocation method and device |
| CN112839382A (en) * | 2020-12-30 | 2021-05-25 | 北京邮电大学 | Video semantic-driven communication and computing resource joint allocation method in Internet of vehicles |
| CN113708962A (en) * | 2021-08-20 | 2021-11-26 | 广东工业大学 | Mobile edge computing resource management method based on network slice |
| CN114816750A (en) * | 2022-04-24 | 2022-07-29 | 江苏鼎集智能科技股份有限公司 | Big data management task operation method |
| CN115033377A (en) * | 2022-05-26 | 2022-09-09 | 北京云思智学科技有限公司 | Service resource prediction method and device based on cluster server and electronic equipment |
| CN117114623A (en) * | 2023-09-18 | 2023-11-24 | 广东泰一高新技术发展有限公司 | Intelligent management method and system for monitoring equipment in park |
Non-Patent Citations (2)
| Title |
|---|
| Chronological and exponential-based Lion optimisation for optimal resource allocation in cloud;Devagnanam, J. 等;INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING;20201127;全文 * |
| 面向优先级任务的移动边缘计算资源分配方法;董思岐;吴嘉慧;李海龙;屈毓锛;胡磊;;计算机工程;20190819(第03期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117472589A (en) | 2024-01-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111966289B (en) | Partition optimization method and system based on Kafka cluster | |
| CN109981744B (en) | Data distribution method and device, storage medium and electronic equipment | |
| CN116761194B (en) | Police affair cooperative communication optimization system and method in wireless communication network | |
| CN118101101B (en) | Cloud computing system and method based on WIFI network | |
| CN114924965A (en) | Performance prediction system for software development based on Internet | |
| CN117527708B (en) | Optimized transmission method and system for enterprise data link based on data flow direction | |
| CN105139157A (en) | Enterprise management method and system based on energy data | |
| CN112867162B (en) | Slice resource allocation method and device | |
| CN112566170B (en) | Network quality evaluation method, device, server and storage medium | |
| CN117793008A (en) | Security management method and device for Internet of things traffic and electric Internet of things system | |
| WO2021147319A1 (en) | Data processing method, apparatus, device, and medium | |
| CN117472589B (en) | Park network service management method and system | |
| CN106372649B (en) | Integrated value Kalman filtering method based on quantization | |
| CN110677463B (en) | Parallel data transmission method, device, medium and electronic equipment | |
| CN110233684B (en) | Signal-to-noise ratio evaluation method, device, equipment and storage medium | |
| CN117354072B (en) | Automatic charging adjustment method and device for data center network bandwidth flow | |
| CN113992249A (en) | Channel state information processing method and device, electronic equipment and storage medium | |
| CN115866235A (en) | Video quality evaluation method and device, electronic equipment and storage medium | |
| CN116647874B (en) | 5G high-performance network server data optimization transmission method and system | |
| CN115473343B (en) | Intelligent gateway multi-master-station parallel access test method | |
| CN118348891B (en) | Intelligent conference centralized control method and system based on information enhancement | |
| CN118802657A (en) | Calculation vector evaluation method, device and storage medium | |
| CN113225228B (en) | Data processing method and device | |
| CN110501952B (en) | Information acquisition equipment and management method | |
| CN111191669B (en) | Data processing method, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A method and system for managing network services in a park Granted publication date: 20240312 Pledgee: China Postal Savings Bank Co.,Ltd. Dongying City Dongying District Branch Pledgor: Shandong Heneng Technology Co.,Ltd. Registration number: Y2024980030294 |