CN109194539B - Data management and control method and device, computer equipment and storage medium - Google Patents
Data management and control method and device, computer equipment and storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/215—Flow control; Congestion control using token-bucket
Abstract
The invention relates to the technical field of computers, and provides a data management and control method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining data to be accessed in a target interface access request, calculating target access data allowed to be accessed by a target interface according to a token bucket algorithm, recording actual flow data when the target access data pass through the target interface, comparing an actual value of each flow parameter of the actual flow data with a standard value, marking the flow parameter of which the actual value is smaller than or equal to the standard value and the corresponding actual value as safety flow data, calculating a predicted value of each flow parameter in the safety flow data, calculating a difference value between the actual value and the predicted value of each flow parameter, and determining that the safety flow data corresponding to the difference value are abnormal flow data if the difference value is larger than a preset target threshold value. The detection accuracy of the abnormal flow data is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data management and control method, an apparatus, a computer device, and a storage medium.
Background
Due to the limited load capacity of the network interface, in order to ensure the flow balance on the interface and reduce the burst flow data on the interface, in order to prevent the situation that the network interface is frequently called by unexpected requests in advance to cause overlarge interface pressure to drag down the network system, and particularly, when large concurrent flow data is accessed, the network interface is easy to crash, the network interface needs to be monitored, abnormal conditions are identified, early warning is timely performed, and the interface pressure is relieved.
However, some conventional anomaly detection methods can only simply identify the size of the total flow data, and cannot distinguish which part of the flow data is normal flow data and which part is abnormal flow data, or can identify specific abnormal flow data, but cannot analyze the cause of the anomaly in time, and particularly when large-scale flow data occurs on a network interface, the efficiency of detecting whether the flow data of the network interface is abnormal is not high, the abnormal flow data cannot be quickly identified, and the accuracy of detecting the abnormal flow data is not high, which is not beneficial to maintenance and management of the interface.
Disclosure of Invention
The embodiment of the invention provides a data management and control method, a data management and control device, computer equipment and a storage medium, and aims to solve the problem of low detection accuracy of abnormal flow data.
A method of data management, comprising:
receiving an interface access request, and acquiring data to be accessed and an accessed target interface in the interface access request;
according to a token bucket algorithm, calculating the number of the replenishable tokens of the data to be accessed, and according to the number of the replenishable tokens, calculating target access data which is allowed to be accessed by the target interface;
monitoring the target access data, and recording actual flow data when the target access data passes through the target interface, wherein the actual flow data comprises flow parameters and actual values of each flow parameter;
acquiring a preset standard value of each flow parameter, comparing the actual value of each flow parameter with the standard value, marking the flow parameters of which the actual values are less than or equal to the standard values and the corresponding actual values as safe flow data, and marking the flow parameters of which the actual values are greater than the standard values and the corresponding actual values as first abnormal flow data;
aiming at the safety flow data, calculating a predicted value of each flow parameter in the safety flow data according to historical flow data in a preset period;
calculating a difference value between the actual value and the predicted value of each flow parameter in the safety flow data, and if the difference value is greater than a preset target threshold value, determining that the safety flow data corresponding to the difference value is second abnormal flow data;
and sending out an early warning prompt according to the first abnormal flow data and the second abnormal flow data.
A data management apparatus, comprising:
the request receiving module is used for receiving an interface access request and acquiring data to be accessed and an accessed target interface in the interface access request;
the token calculation module is used for calculating the number of the replenishable tokens of the data to be accessed according to a token bucket algorithm and calculating target access data which is allowed to be accessed by the target interface according to the number of the replenishable tokens;
the data recording module is used for monitoring the target access data and recording actual flow data when the target access data passes through the target interface, wherein the actual flow data comprises flow parameters and actual values of each flow parameter;
the abnormal marking module is used for acquiring a preset standard value of each flow parameter, comparing the actual value of each flow parameter with the standard value, marking the flow parameter of which the actual value is less than or equal to the standard value and the corresponding actual value as safe flow data, and marking the flow parameter of which the actual value is greater than the standard value and the corresponding actual value as first abnormal flow data;
the data calculation module is used for calculating a predicted value of each flow parameter in the safety flow data according to historical flow data in a preset period aiming at the safety flow data;
the abnormal determination module is used for calculating a difference value between the actual value and the predicted value of each flow parameter in the safety flow data, and if the difference value is larger than a preset target threshold value, the safety flow data corresponding to the difference value is determined to be second abnormal flow data;
and the early warning prompt module is used for sending out early warning prompts according to the first abnormal flow data and the second abnormal flow data.
A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the above data processing method when executing said computer program. A computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described data management method.
The data control method, the data control device, the computer equipment and the storage medium can realize access limitation of the data to be accessed to the target interface by acquiring the data to be accessed in the interface access request and calculating the number of the replenishable tokens of the data to be accessed according to the token bucket algorithm in a way of calculating the number of the replenishable tokens in real time, can effectively control interface flow data, ensure data balance of the target interface to be accessed, maintain balance of the target interface, relieve pressure of the target interface and improve use efficiency of the target interface, and on the basis, by monitoring the target access data and recording actual flow data when the target access data passes through the target interface, compare the actual value of each flow parameter in the actual flow data with a standard value, mark the flow parameter of which the actual value is greater than the standard value and the corresponding actual value as first abnormal flow data, recording the flow parameter with the actual value less than or equal to the standard value and the corresponding actual value as safety flow data, then calculating a predicted value of the safety flow data according to historical data in a preset period, and further determining second abnormal flow data existing in the safety flow data according to the actual value of the safety flow data and the predicted value corresponding to the actual value of the safety flow data; meanwhile, an early warning prompt is sent to abnormal flow data, so that a manager can conveniently take corresponding flow control measures to the target interface according to the early warning prompt, and the maintainability of the target interface is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a data management method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a data management method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an implementation of step S20 in the data management method according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating an implementation of step S204 in the data management and control method according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating an implementation of step S50 in the data management method according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating an implementation of step S70 in the data management method according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a data management apparatus according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a computer device according to an embodiment of the 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 some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 illustrates an application environment provided by an embodiment of the present invention, which includes a server and a client, where the server and the client are connected through a network, and the client is configured to receive an interface access request and send the interface access request to the server, and the client may specifically be, but not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices; the server is used for processing the traffic data, and the server can be specifically realized by an independent server or a server cluster formed by a plurality of servers. The data management and control method provided by the embodiment of the invention is applied to the server side.
In order to illustrate the technical solution of the present invention, the following description will be given by way of specific examples. Before describing the specific embodiments, the concepts of different types of data used in the embodiments of the present invention are explained as follows:
the data to be accessed refers to data requested to pass through the target interface.
Target access data refers to data that is actually allowed through the target interface.
The actual flow data, the safety flow data, the abnormal flow data and the historical flow data refer to flow indexes and data values thereof, and specifically include flow parameters and flow parameter values.
Referring to fig. 2, fig. 2 shows an implementation flow of the data management and control method provided in this embodiment. The details are as follows:
s10: and receiving an interface access request, and acquiring data to be accessed and an accessed target interface in the interface access request.
In this embodiment, the received interface access request includes a data source IP address, an interface number to be accessed, a protocol type used by the access interface, data to be accessed, and the like, where the data to be accessed is data actually requested to pass through the target interface.
For example, the data source IP address is "192.168.0.29", the interface number is "80 interface" or "21 interface", etc., and the protocol type is "UDP protocol".
S20: and calculating the number of the replenishable tokens of the data to be accessed according to the token bucket algorithm, and calculating target access data which is allowed to be accessed by the target interface according to the number of the replenishable tokens.
In this embodiment, the token bucket algorithm means that the token bucket adds tokens to the token bucket at a constant 1/QPS time interval, and if the token bucket is full, the tokens are not added, and if an access request is received, each access task in the request consumes one token.
Qps (query Per second) is "query rate Per second", which is the number of queries that a server can respond to Per second, and is a measure of how much traffic a particular query server processes within a specified time, and is used for analyzing traffic data. The number of replenishable tokens refers to the maximum number of tokens that the token bucket can add when an access request is received. Target access data refers to data that the target interface actually allows to pass.
Further, in this embodiment, on the basis of the original algorithm of the token bucket, a method for calculating the number of tokens that can be supplemented by the token bucket in real time through calculating a time difference is added, and control of the rate at which data passes through the target interface can be realized.
S30: monitoring target access data, and recording actual flow data when the target access data passes through a target interface, wherein the actual flow data comprises flow parameters and actual values of each flow parameter.
In this embodiment, the traffic parameters in the actual traffic data include the number of bytes, the number of packets, the number of times the interface can be accessed, and the rate at which the target access data passes through the interface.
Specifically, the actual data information of the target access data through the target interface is recorded in a data log of the database in real time, and the actual value of each flow parameter can be calculated according to the counted actual data information, so that the data information of the flow data can be conveniently acquired and analyzed.
For example, counting the number of packets passing through the destination interface for one minute of destination access data, the rate of the flow parameter "number of packets" passing through the destination interface in one minute may be calculated, and if the actual value of the flow parameter "number of packets" passing through the destination interface in one minute is counted as "120 k", the time "one minute" may be divided by the number of packets "120 k", so as to obtain the rate of the flow parameter "number of packets" passing through the destination interface in one minute as 2 k/s.
S40: and acquiring a preset standard value of each flow parameter, comparing the actual value of each flow parameter with the standard value, marking the flow parameters of which the actual values are less than or equal to the standard values and the corresponding actual values as safe flow data, and marking the flow parameters of which the actual values are greater than the standard values and the corresponding actual values as first abnormal flow data.
Specifically, a preset standard value of each flow parameter is obtained from the database, the actual value of each flow parameter is compared with the standard value of the flow parameter, the flow parameter with the actual value larger than the standard value and the corresponding actual value are marked as first abnormal flow data, and obvious abnormal flow data in the actual flow data are quickly identified.
For example, if an actual flow data passing through the "124 interface" includes a flow parameter "packet number", an actual value of the flow parameter "packet number" is "400", a flow parameter is "byte number", an actual value of the flow parameter "packet number" is "20000", a standard value "350" of the flow parameter "packet number" of the "124 interface" in the database, and a standard value "21000" of the flow parameter "byte number", the actual value of each flow parameter is compared with the standard value of the flow parameter, and the actual value of the "packet number" is greater than the standard value thereof, the flow parameter "packet number" and the corresponding actual value "400" are marked as first abnormal flow data, and the actual value of the "byte number" is less than the standard value thereof, and the flow parameter "byte number" and the corresponding actual value "20000" are marked as safe flow data.
It should be noted that the standard value of the flow parameter can be set according to the actual application requirement, and is not limited herein.
S50: and aiming at the safety flow data, calculating a predicted value of each flow parameter in the safety flow data according to historical flow data in a preset period.
In this embodiment, the historical traffic data is actual traffic data when the target access data recorded in the data log of the database in a preset period passes through the target interface, and the historical traffic data includes the historical traffic parameters and the actual value of each historical traffic parameter.
Specifically, for each flow parameter in the safety flow data, an actual value of each flow parameter of the historical flow data passing through the interface in a preset period may be multiplied by a preset flow regulation parameter, and the obtained product is used as a predicted value for predicting whether the current flow parameter is the normal flow data, and in the subsequent steps, the predicted value is used to further detect abnormal flow data possibly existing in the safety flow data, so that the accuracy of detecting the abnormal flow data can be improved.
S60: and calculating a difference value between the actual value and the predicted value of each flow parameter in the safety flow data, and if the difference value is greater than a preset target threshold value, determining the safety flow data corresponding to the difference value as second abnormal flow data.
Specifically, a difference value between an actual value and a predicted value of each flow parameter in the safety flow data is calculated, the difference value is compared with a preset target threshold value, the flow parameter corresponding to the difference value larger than the target threshold value and the actual value of the flow parameter are determined to be second abnormal flow data, the abnormal flow data existing in the safety flow data are rapidly identified, the accuracy of identifying the abnormal flow data is guaranteed, and the accuracy of detecting the abnormal flow data is improved.
For example, if the actual value of a flow parameter "the rate of flow data passing through the target interface" is "250 kb/s", the predicted value obtained by calculation is "200 kb/s", the difference between the actual value and the corresponding predicted value is "50", the obtained preset target threshold value is "30", and the difference is greater than the preset target threshold value, the flow parameter "the rate of flow data passing through the target interface" and the actual value "250 kb/s" are determined as the second abnormal flow data.
It should be noted that the preset target threshold may be set according to the actual application requirement, and is not limited herein.
S70: and sending out an early warning prompt according to the first abnormal flow data and the second abnormal flow data.
In this embodiment, the manner of the warning prompt sent out may include, but is not limited to, an email, a short message, or an instant message, and may also be other manners, which are not limited herein.
Specifically, according to the first abnormal flow data and the second abnormal flow data, the flow parameters marked as the first abnormal flow data and the second abnormal flow data, the actual values of the flow parameters, and the preset standard values corresponding to the flow parameters are included in the content of the early warning prompt, and the number of the actual values exceeding the standard values, the data source IP addresses and the interface numbers corresponding to the flow parameters are noted so that the manager can take corresponding measures to process the abnormal flow data according to the content of the early warning prompt.
In the embodiment, by adopting a token bucket algorithm to calculate the number of the supplementary tokens in real time for obtaining the data to be accessed in the interface access request, the access limitation of the data to be accessed to the target interface is realized, the effective control on the interface flow data can be achieved, the data balance of the access target interface is ensured, the balance of the target interface is maintained, the pressure of the target interface is relieved, and the use efficiency of the target interface is improved, and on the basis, the actual flow data when the target access data passes through the target interface is monitored and recorded, the actual value of each flow parameter in the actual flow data is compared with the standard value, the flow parameter of which the actual value is greater than the standard value and the corresponding actual value are marked as first abnormal flow data, the flow parameter of which the actual value is less than or equal to the standard value and the corresponding actual value are marked as safe flow data, the method has the advantages that the obvious abnormal data in the actual flow data can be quickly identified, then the predicted value of the safety flow data is calculated according to the historical data in the preset period, and the second abnormal flow data in the safety flow data is further determined according to the actual value and the predicted value of the safety flow data; meanwhile, an early warning prompt is sent to abnormal flow data, so that a manager can conveniently take corresponding flow control measures to the target interface according to the early warning prompt, and the maintainability of the target interface is improved.
In an embodiment, as shown in fig. 3, in step S20, that is, calculating the number of replenishable tokens of the data to be accessed according to the token bucket algorithm, and calculating the target access data that the target interface allows the data to be accessed to access according to the number of replenishable tokens specifically includes the following steps:
s201: and acquiring the total number A of access tasks contained in the data to be accessed, wherein A is a positive integer.
In this embodiment, to avoid the failure of the target interface caused by too many access requests, current limiting, that is, controlling the number of access requests, is required. The current limiting method of the target interface includes, but is not limited to: a counting algorithm, a leaky bucket algorithm, a token bucket algorithm, and the like. The token bucket algorithm is one of the most commonly used algorithms in network traffic shaping (TrafficShaping) and Rate Limiting (Rate Limiting).
Preferably, the embodiment adopts an improved algorithm of the token bucket algorithm, and the detailed implementation process is described in the content of step S202 to step S206.
The token bucket algorithm is used for controlling the number of executed access requests, and the principle is as follows: each access request consumes a fixed number of tokens, the capacity of the token bucket, i.e. the upper limit of the number of tokens accommodated, is fixed, and the token bucket may itself generate tokens at a constant rate and continuously. If tokens are not consumed, or are consumed less than generated, tokens are continually incremented until the bucket is filled. Tokens that are later regenerated will overflow the bucket. The number of tokens that can be held in the last bucket never exceeds the capacity of the token bucket.
Specifically, according to the principle that one access task consumes one token in the token bucket algorithm, the total number of the access tasks contained in the data to be accessed is obtained, so that the calculation of the number of the tokens which can be supplemented in the token bucket in the subsequent steps is facilitated.
S202: a time point of the supplementary token and a current time point are acquired, and a time interval deltat between the current time point and the time point of the supplementary token is calculated.
Specifically, the server has an upper limit on its throughput due to limitations of hardware configuration, network speed, technical bottlenecks, and the like, and in order to ensure normal use of the target interface, it needs to define a Query Per Second rate (QPS) of the target interface, and therefore, after receiving an interface access request sent by a user each time, it needs to acquire a time interval between a current time point and a time point of supplementing a token. The time interval can be calculated in real time, so that the number of the replenishable tokens of the token bucket can be calculated in real time in the subsequent step.
Wherein, the query rate per second refers to the number of access requests processed by the server in a specified time.
And the time point of the supplementary token is the time point corresponding to the last supplementary token.
For example, in one embodiment, the current time point is 17:03:21, the supplemental token time point is 17:02:14, and the corresponding time interval is 67 seconds.
S203: and comparing the time interval delta T with a preset interval threshold value T to obtain a comparison result.
Specifically, the preset interval threshold is a value of a preset time interval, specifically, may be a time required to fill the whole token bucket according to a fixed filling rate, where the filling rate may be specifically set according to a query rate per second of the target interface and a throughput of the server, and it is understood that the filling rates corresponding to different target interfaces may be different. For example, the preset interval threshold may be set to 6 seconds, or may be set according to actual situations, and is not particularly limited herein.
The comparison result is a magnitude result between the time interval Δ T and the preset interval threshold T, specifically, the time interval Δ T may be greater than or equal to the preset interval threshold T, or the time interval Δ T may be smaller than the preset interval threshold T.
It should be noted that, the comparison between the time interval and the preset interval threshold T is performed to fully know how fast the data to be accessed uses the target interface, and is used to determine whether to perform traffic data limitation on the traffic data to be accessed.
S204: and calculating the number M of the supplementary tokens according to a preset filling mode corresponding to the comparison result.
Specifically, according to the comparison result obtained in step S203, the number of the replenishable tokens is calculated according to the preset filling manner corresponding to the comparison result.
Different filling modes can be set according to different comparison results, the different filling modes can influence the use efficiency of the target interface, the specific filling mode can be set according to actual situations, and no specific limitation is made here.
For example, in one embodiment, when the comparison result is that the time interval Δ T is greater than or equal to the preset interval threshold T, the token bucket is filled in by using a first preset filling manner, and when the comparison result is that the time interval Δ T is less than the preset interval threshold T, a product of the time interval Δ T and the query rate per second is calculated, and the product is used as the number M of the replenishable tokens.
It should be noted that, in the embodiment, steps S202 to S206 adopt an improved algorithm of a token bucket algorithm, a time interval between a current time point and a time point of supplementing a token is compared with a preset interval threshold to obtain a comparison result, and then the filling amount is determined according to the comparison result, so that resources of a target interface are fully utilized, and meanwhile, a problem of interface paralysis caused by an access request of the target interface exceeding the processing capability of a server is avoided, and a characteristic of high availability of the interface is ensured.
S205: and if M is less than A, calculating a difference value N between A and M, selecting N access tasks from the data to be accessed as access refusing tasks, and terminating the access of the access refusing tasks to the target interface, wherein N is a positive integer.
Specifically, if the number M of the replenishable tokens is less than the total number a of the access tasks included in the data to be accessed, it indicates that the current state of the target interface cannot process all the access tasks in the data to be accessed, at this time, the number N of the access tasks that cannot be completed is obtained by calculating the difference between the number of the replenishable tokens and the total number of the access tasks included in the data to be accessed, and N access requests are selected from the data to be accessed as access-denied tasks, and the access of the access-denied tasks to the target interface is terminated.
For example, assuming that the total number of access tasks is "80", the calculated number of replenishable tokens is "60", and the difference between the total number of access tasks and the number of replenishable tokens is "20", then "20" access tasks are selected from all the tasks of the access tasks as the access-denied tasks, and the access of the "20" access-denied tasks to the target interface is terminated.
It should be noted that, if the number of the replenishable tokens is greater than or equal to the total number of the access tasks included in the data to be accessed, it indicates that the current state of the target interface can process all the access tasks in the data to be accessed.
S206: and removing the access refusing task from the data to be accessed, and taking the data to be accessed after the access refusing task is removed as target access data.
Specifically, the task to be denied access is removed from the data to be accessed, and the data to be accessed after the task to be denied access is removed is used as target access data, so that the updated target access data is the maximum number of access data which can be processed by the target interface within a preset interval threshold, and the use efficiency of the target interface is improved.
For example, continuing with the example of step S205, the total number of access tasks is "80", and "20" access denied tasks are removed, resulting in the data to be accessed with the access task "60" as the target access data.
In this embodiment, by calculating the time interval between the obtained current time point and the time point of the supplementary token, the number of the supplementary tokens of the token bucket in the subsequent steps can be calculated in real time in such a way that the time interval is calculated in real time, meanwhile, the time interval is compared with a preset interval threshold, the number of the supplementary tokens is calculated according to a preset filling mode corresponding to the comparison result, and if the number of the supplementary tokens is less than the obtained total number of the access tasks, the update of the data to be accessed is realized by calculating the difference between the number of the supplementary tokens and the total number of the access tasks, so that the limitation on the access target interface of the flow data is realized, the balance of the target interface is favorably maintained, and the anomaly identification and analysis of the subsequent flow data are favorably carried out.
In an embodiment, as shown in fig. 4, the step S204 of calculating the number M of the replenishable tokens according to the preset filling manner corresponding to the comparison result specifically includes the following steps:
s2041: and if the comparison result is that delta T is larger than or equal to T, acquiring the current token quantity E and a preset token upper limit F, and calculating the quantity M of the tokens which can be supplemented according to a formula M-F-E.
Specifically, the server managing the target interface fills tokens in the token bucket at a fixed rate, and when a time interval Δ T between a current time point and a time point of token supplementation is greater than or equal to a preset interval threshold T, the server has sufficient filling time to fill the token bucket, which can be understood as a condition that the frequency of using the target interface by the data to be accessed is low and the data to be accessed does not need to be subjected to traffic data limitation, and at this time, the number M of the tokens that can be supplemented is a difference value between an upper limit F of the tokens in the token bucket and a current number E of the tokens.
The current token number refers to the number of tokens remaining in the token bucket before filling, and the preset upper token limit is the capacity of the token bucket mentioned in step S201.
For example, in a specific embodiment, the preset interval threshold is 10 seconds, the preset upper limit of tokens is 150, after an interface access request sent by a user is received, the tokens in the token bucket need to be replenished, the time interval between the current time and the time point of replenishing the tokens is calculated to be 13 seconds, and the number of current tokens in the token bucket is 120, it is easy to understand that, since the time interval between two times of replenishing the tokens is greater than the preset interval threshold, the token replenishing will fill the token bucket, and at this time, the number of replenishable tokens is 30.
S2042: if the comparison result is that delta T is less than T, the number M of the supplementary tokens is calculated according to the following formula:
specifically, when the time interval between the current time point and the time point of supplementing the token is smaller than the preset interval threshold, it cannot be determined whether the token bucket is filled, the limitation of traffic data on the data to be accessed needs to be performed, and the use of the data to be accessed on the target interface is reduced
Number of tokens that can be replenished at most
And calculating the maximum token number F-E which can be supplemented by the token bucket at the moment if
The replenishable quantity is
If it is
If the number of tokens is larger than or equal to F-E, the token bucket can be filled up at most, and the number of tokens can be supplemented to F-E.
It should be noted that, when the time interval is smaller than the preset interval threshold, the maximum number of tokens that can be supplemented and the maximum number of tokens that can be supplemented are calculated and compared, and the smaller one of the calculated numbers is taken as the number of tokens that can be supplemented, so that the processing capability of the server for managing the target interface is fully utilized, and the abnormality caused by excessive interface access requests is avoided.
Continuing with the example in step S2041, after another interface access request sent by the user is received, the time interval between the current time and the time point of replenishing tokens is calculated to be 4 seconds, the number of current tokens in the token bucket is 120, and according to the above description, it is easy to calculate that the maximum number of tokens that can be replenished is 60, and the maximum number of tokens that can be replenished is 30, so that the number of tokens that can be replenished this time is 30.
In this embodiment, for the two different comparison results obtained in step S203, an improved algorithm of the token bucket is used to perform current limiting, so that resources of the target interface can be fully utilized, and meanwhile, an anomaly of the target interface caused by short-time high-frequency access is avoided, which is beneficial to maintaining the high availability characteristic of the target interface.
In an embodiment, as shown in fig. 5, in step S50, the preset period includes H sub-periods, each sub-period includes k preset time periods, where H and k are positive integers, that is, for the safety flow data, the step of calculating the predicted value of each flow parameter in the safety flow data according to the historical flow data in the preset period specifically includes the following steps:
s501: calculating the stage predicted value of each flow parameter in the safety flow data in each preset time period according to the following formula:
S'(i,h)＝αT(i,h)+(1-α)S'(i,h-1)
S”(i,h)＝αS'(i,h)+(1-α)S”(i,h-1)
wherein the value range of i is (0, k)]H is the H-th sub-period in the preset period, H is a positive integer, and the value range of H is [1, H]α is a predetermined sensitivity coefficient of prediction, T(i,h)For the actual value, p, of each flow parameter in the safety flow data in the ith preset time period of the h sub-period(i,h+1)And predicting the phase value of each flow parameter in the safety flow data in the ith preset time period of the h +1 th sub-period.
In this embodiment, the preset period includes H sub-periods, each of which includes k preset time periods, where H and k are positive integers.
Specifically, in the ith preset time period, the safe flow is calculated according to the actual value of each flow parameter in the historical data in the 1 st sub-period and the preset prediction sensitivity coefficient alphaSimilarly, the predicted value of the next sub-period can be calculated according to the historical data of each sub-period until the actual value T of each flow parameter in the historical data in the H sub-period(i,H)Calculating the stage predicted value P of each flow parameter in the safety flow data in the H +1 th sub-period(i,H+1)And wherein in the ith period in the 1 st cycle is S'(i,0)And S "(i,0)Respectively as follows: s'(i,0)＝S”(i,0)＝T(i,1)。
It can be understood that, according to the historical data in the preset period, the phase predicted value of each flow parameter in the safety flow data in each sub-period is calculated according to the above formula, and the phase predicted value of each flow parameter in the safety flow data in each sub-period in the ith preset time period can be accurately calculated, so that the capability of the target interface for processing the data of each flow parameter at most in each sub-period phase can be reflected.
For example, assume that the actual value T of a traffic parameter "number of packets" in the historical data in the 1 st preset time period of the 1 st sub-cycle(1,1)The safety flow data is 100, the preset prediction sensitivity coefficient alpha is 0.2, and the predicted value P of the flow parameter data packet number in the safety flow data in the 1 st preset time period of the 2 nd sub-period is calculated according to the formula(1,2)Is "100".
It should be noted that the preset prediction sensitivity coefficient α may be obtained by dividing the variation range of the historical data by the variation range of the sensitive factor, and the sensitive factor may be set according to the actual application requirement, which is not limited herein.
S502: calculating the average value of the predicted values of the flow parameters in the H stages in each preset time period according to the following formula, and taking the average value as the predicted value of the period:
wherein, P(i)And predicting the cycle prediction value of each flow parameter in the safety flow data in the ith preset time period.
Specifically, in the ith preset time period, the phase predicted values of each flow parameter in the safety flow data of each sub-period are summed and divided by the total number of the sub-periods to obtain an average value of the phase predicted values of H sub-periods in the preset time period.
It should be noted that by calculating each flow parameter in the safety flow data, the cycle prediction value of the phase prediction value of H sub-cycles in the preset time period can be counted and reflected on the ith preset time period in the preset cycle, and the general level and the data concentration trend of the phase prediction value of each flow parameter in the safety flow data are convenient for further determining the prediction value of each flow parameter in the safety flow data in the following.
S503: according to the period predicted value, calculating the average value of k period predicted values of each flow parameter in the safety flow data according to the following formula, and determining the average value as the predicted value of each flow parameter:
and P is a predicted value of each flow parameter in the safety flow data.
Specifically, the cycle predicted values of each preset time period obtained in step S502 are summed and divided by the total number of the preset time periods to obtain an average value of k cycle predicted values of each flow parameter in the safety flow data in the preset cycle, so that the predicted value of each flow parameter in the safety flow data is accurately calculated according to the condition of the historical flow data, and the accuracy and the comparability of the predicted values are ensured.
In this embodiment, the predicted value of each flow parameter in the safety flow data can be accurately calculated according to the historical flow data of different target interfaces, so that the abnormal flow data existing in the safety flow data can be further identified by using the predicted value subsequently, and the accuracy of detecting the abnormal flow data is improved.
In an embodiment, as shown in fig. 6, in step S70, the step of sending the warning prompt according to the first abnormal traffic data and the second abnormal traffic data includes the following steps:
s701: and counting the target data source IP address and the target interface number corresponding to the abnormal flow data according to the interface access request.
Specifically, because the received interface access request includes the data source IP address and the interface number corresponding to the traffic data, the target data source IP address and the target interface number corresponding to the abnormal traffic data can be counted, so that the target interface corresponding to the abnormal traffic data can be quickly found through the target data source IP address and the target interface number, and an effective traffic control instruction can be sent to the corresponding target interface for the abnormal traffic data in the following process.
S702: and sending an access blocking instruction to a target interface corresponding to the target data source IP address and the target interface number, blocking the continuous access of the abnormal flow data to the target interface, and sending an early warning prompt.
Specifically, in order to maintain the stability of the target interface and ensure the use balance of the target interface, an access blocking instruction may be sent to the target interface corresponding to the target data source IP address and the target interface number, where the access blocking instruction is used to block the continued access of the abnormal flow data to the target interface, and meanwhile, an early warning prompt may be sent, so that a manager may take corresponding flow control measures to the target interface according to the early warning prompt, thereby implementing the maintenance and management of the target interface.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a data management and control apparatus is provided, and the data management and control apparatus corresponds to the data management and control method in the above embodiments one to one. As shown in fig. 7, the data management and control apparatus includes a request receiving module 701, a token calculating module 702, a data recording module 703, an anomaly marking module 704, a data calculating module 705, an anomaly determining module 706, and an early warning prompting module 707. The functional modules are explained in detail as follows:
a request receiving module 701, configured to receive an interface access request, and obtain data to be accessed and an accessed target interface in the interface access request;
the token calculation module 702 is configured to calculate the number of replenishable tokens of the data to be accessed according to a token bucket algorithm, and calculate target access data that the target interface allows the data to be accessed to access according to the number of replenishable tokens;
the data recording module 703 is configured to monitor the target access data and record actual traffic data when the target access data passes through the target interface, where the actual traffic data includes traffic parameters and an actual value of each traffic parameter;
the abnormal marking module 704 is configured to obtain a preset standard value of each flow parameter, compare the actual value of each flow parameter with the standard value, mark the flow parameter of which the actual value is less than or equal to the standard value and the corresponding actual value as safe flow data, and mark the flow parameter of which the actual value is greater than the standard value and the corresponding actual value as first abnormal flow data;
the data calculation module 705 is configured to calculate, for the safety traffic data, a predicted value of each traffic parameter in the safety traffic data according to historical traffic data in a preset period;
an anomaly determination module 706, configured to calculate a difference between an actual value and a predicted value of each flow parameter in the safety flow data, and if the difference is greater than a preset target threshold, determine that the safety flow data corresponding to the difference is second abnormal flow data;
and the early warning prompt module 707 is configured to send an early warning prompt according to the first abnormal flow data and the second abnormal flow data.
Further, the token calculation module 702 includes:
a task obtaining unit 7021, configured to obtain a total number a of access tasks included in the data to be accessed, where a is a positive integer;
a time obtaining unit 7022, configured to obtain a time point of the supplementary token and a current time point, and calculate a time interval Δ t between the current time point and the time point of the supplementary token;
a time comparing unit 7023, configured to compare the time interval Δ T with a preset interval threshold T to obtain a comparison result;
a quantity calculating unit 7024, configured to calculate a quantity M of the replenishable tokens according to a preset filling manner corresponding to the comparison result;
an access rejection unit 7025, configured to calculate a difference N between a and M if M is less than a, select N access tasks from the data to be accessed as access rejection tasks, and terminate access to the target interface by the access rejection tasks, where N is a positive integer;
a task removing unit 7026, configured to remove the access denied task from the data to be accessed, and use the data to be accessed after the access denied task is removed as target access data.
Further, the number calculation unit 7024 includes:
the first comparing subunit 70241, configured to, if the comparison result is that Δ T is greater than or equal to T, obtain the current token number E and a preset token upper limit F, and calculate the number M of tokens that can be supplemented according to a formula M — F-E;
a second comparing subunit 70242, configured to, if the comparison result is Δ T < T, calculate the number of tokens that can be complemented M according to the following formula:
further, the data calculation module 705 includes:
the phase calculating unit 7051 is configured to calculate a phase predicted value of each flow parameter in the safety flow data in each preset time period according to the following formula:
S'(i,h)＝αT(i,h)+(1-α)S'(i,h-1)
S”(i,h)＝αS'(i,h)+(1-α)S”(i,h-1)
wherein the value range of i is (0, k)]H is the H-th sub-period in the preset period, H is a positive integer, and the value range of H is [1, H]α is a predetermined sensitivity coefficient of prediction, T(i,h)For the actual value, p, of each flow parameter in the safety flow data in the ith preset time period of the h sub-period(i,h+1)The phase prediction value of each flow parameter in the safety flow data in the ith preset time period of the h +1 th sub-period is obtained;
a period calculating unit 7052, configured to calculate an average value of the predicted values of the H stages of each flow parameter in each preset time period according to the following formula, and use the average value as the predicted value of the period:
wherein, P(i)The period prediction value of each flow parameter in the safety flow data in the ith preset time period is obtained;
an average value calculating unit 7053, configured to calculate an average value of k cycle predicted values of each flow parameter in the safety flow data according to the following formula according to the cycle predicted values, and determine the average value as the predicted value of each flow parameter:
and P is a predicted value of each flow parameter in the safety flow data.
Further, the early warning module 707 includes:
the data statistics unit 7071 is configured to, according to the interface access request, count a target data source IP address and a target interface number corresponding to the abnormal traffic data;
and the instruction sending unit 7072 is configured to send an access blocking instruction to a target interface corresponding to the target data source IP address and the target interface number, block the target interface from being continuously accessed by the abnormal traffic data, and send an early warning prompt.
For specific limitations of the data management and control apparatus, reference may be made to the above limitations of the data management and control method, which is not described herein again. The modules in the data management and control device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store flow data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data management method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the data management method of the above embodiments are implemented, for example, steps S10 to S70 shown in fig. 2. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units of the data management apparatus in the above embodiments, such as the functions of the modules 701 to 707 shown in fig. 7. To avoid repetition, further description is omitted here.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when being executed by a processor, implements the data management and control method in the above method embodiment, or the computer program, when being executed by the processor, implements the functions of each module/unit in the data management and control device in the above device embodiment. To avoid repetition, further description is omitted here.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (8)
1. A data management and control method is characterized by comprising the following steps:
receiving an interface access request, and acquiring data to be accessed and an accessed target interface in the interface access request;
according to a token bucket algorithm, calculating the number of the replenishable tokens of the data to be accessed, and according to the number of the replenishable tokens, calculating target access data which is allowed to be accessed by the target interface;
monitoring the target access data, and recording actual flow data when the target access data passes through the target interface, wherein the actual flow data comprises flow parameters and actual values of each flow parameter;
acquiring a preset standard value of each flow parameter, comparing the actual value of each flow parameter with the standard value, marking the flow parameters of which the actual values are less than or equal to the standard values and the corresponding actual values as safe flow data, and marking the flow parameters of which the actual values are greater than the standard values and the corresponding actual values as first abnormal flow data;
aiming at the safety flow data, calculating a predicted value of each flow parameter in the safety flow data according to historical flow data in a preset period;
calculating a difference value between the actual value and the predicted value of each flow parameter in the safety flow data, and if the difference value is greater than a preset target threshold value, determining that the safety flow data corresponding to the difference value is second abnormal flow data;
sending out an early warning prompt according to the first abnormal flow data and the second abnormal flow data;
the calculating the number of the replenishable tokens of the data to be accessed according to the token bucket algorithm, and calculating the target access data which is allowed to be accessed by the target interface according to the number of the replenishable tokens comprises:
acquiring the total number A of access tasks contained in the data to be accessed, wherein A is a positive integer;
acquiring a time point of a supplementary token and a current time point, and calculating a time interval delta t between the current time point and the time point of the supplementary token;
comparing the time interval delta T with a preset interval threshold value T to obtain a comparison result;
calculating the number M of the tokens capable of being supplemented according to a preset filling mode corresponding to the comparison result;
if M is less than A, calculating a difference value N between A and M, selecting N access tasks from the data to be accessed as access refusing tasks, and terminating the access of the access refusing tasks to the target interface, wherein N is a positive integer;
and removing the access refusing task from the data to be accessed, and taking the data to be accessed after the access refusing task is removed as the target access data.
2. The data management and control method according to claim 1, wherein the calculating the number M of the replenishable tokens according to the preset filling manner corresponding to the comparison result includes:
if the comparison result is that delta T is larger than or equal to T, acquiring the current token quantity E and a preset token upper limit F, and calculating the number M of the tokens capable of being supplemented according to a formula M-F-E;
if the comparison result is that delta T is less than T, calculating the number M of the replenishable tokens according to the following formula:
3. the data management and control method according to claim 1, wherein the interface access request includes a data source IP address and an interface number to be accessed, and the sending an early warning prompt according to the first abnormal traffic data and the second abnormal traffic data includes:
according to the interface access request, counting a target data source IP address and a target interface number corresponding to the abnormal flow data;
and sending an access blocking instruction to a target interface corresponding to the target data source IP address and the target interface number, blocking the continuous access of the abnormal flow data to the target interface, and sending an early warning prompt.
4. The data management and control method according to claim 1, wherein the preset period includes H sub-periods, each sub-period includes k preset time periods, where H and k are positive integers, and the calculating, for the safe traffic data, the predicted value of each of the traffic parameters in the safe traffic data according to historical traffic data in the preset period includes:
calculating the phase predicted value of each flow parameter in the safety flow data in each preset time period according to the following formula:
S'(i,h)＝αT(i,h)+(1-α)S'(i,h-1)
S”(i,h)＝αS'(i,h)+(1-α)S”(i,h-1)
wherein the value range of i is (0, k)]H is the H-th sub-period in the preset period, H is a positive integer, and the value range of H is [1, H]α is a predetermined sensitivity coefficient of prediction, T(i,h)Is that it isActual value, P, of each flow parameter in the safety flow data in the ith preset time period of the h-th sub-period(i,h+1)Phase prediction values of each flow parameter in the safety flow data in the ith preset time period of the h +1 th sub-period, wherein S 'is in the ith time period of the 1 st sub-period'(i,0)And S "(i,0)Respectively as follows: s'(i,0)＝S”(i,0)＝T(i,1)；
Calculating the average value of the H stage predicted values of each flow parameter in each preset time period according to the following formula, and taking the average value as a cycle predicted value:
wherein, P(i)Predicting a cycle prediction value of each flow parameter in the safety flow data in an ith preset time period;
according to the period predicted value, calculating the average value of k period predicted values of each flow parameter in the safety flow data according to the following formula, and determining the average value as the predicted value of each flow parameter:
and P is a predicted value of each flow parameter in the safety flow data.
5. A data management and control apparatus, comprising:
the request receiving module is used for receiving an interface access request and acquiring data to be accessed and an accessed target interface in the interface access request;
the token calculation module is used for calculating the number of the replenishable tokens of the data to be accessed according to a token bucket algorithm and calculating target access data which is allowed to be accessed by the target interface according to the number of the replenishable tokens;
the data recording module is used for monitoring the target access data and recording actual flow data when the target access data passes through the target interface, wherein the actual flow data comprises flow parameters and actual values of each flow parameter;
the abnormal marking module is used for acquiring a preset standard value of each flow parameter, comparing the actual value of each flow parameter with the standard value, marking the flow parameter of which the actual value is less than or equal to the standard value and the corresponding actual value as safe flow data, and marking the flow parameter of which the actual value is greater than the standard value and the corresponding actual value as first abnormal flow data;
the data calculation module is used for calculating a predicted value of each flow parameter in the safety flow data according to historical flow data in a preset period aiming at the safety flow data;
the abnormal determination module is used for calculating a difference value between the actual value and the predicted value of each flow parameter in the safety flow data, and if the difference value is larger than a preset target threshold value, the safety flow data corresponding to the difference value is determined to be second abnormal flow data;
the early warning prompting module is used for sending out early warning prompts according to the first abnormal flow data and the second abnormal flow data;
the token computation module includes:
the task obtaining unit is used for obtaining the total number A of access tasks contained in the data to be accessed, wherein A is a positive integer;
the time acquisition unit is used for acquiring a time point of a supplementary token and a current time point and calculating a time interval delta t between the current time point and the time point of the supplementary token;
the time comparison unit is used for comparing the time interval delta T with a preset interval threshold value T to obtain a comparison result;
the quantity calculating unit is used for calculating the quantity M of the replenishable tokens according to the preset filling mode corresponding to the comparison result;
the access rejection unit is used for calculating a difference value N between the A and the M if the M is less than the A, selecting N access tasks from the data to be accessed as access rejection tasks, and terminating the access of the access rejection tasks to the target interface, wherein N is a positive integer;
and the task removing unit is used for removing the access refusing task from the data to be accessed and taking the data to be accessed after the access refusing task is removed as the target access data.
6. The data management and control apparatus according to claim 5, wherein the warning prompt module includes:
the data statistics unit is used for carrying out statistics on a target data source IP address and a target interface number corresponding to the abnormal flow data according to the interface access request;
and the instruction sending unit is used for sending an access blocking instruction to a target interface corresponding to the target data source IP address and the target interface number, blocking the continuous access of the abnormal flow data to the target interface, and sending an early warning prompt.
7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the data administration method according to any one of claims 1 to 4 when executing the computer program.
8. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the data management method according to any one of claims 1 to 4.
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