CN109714793B - Load adjustment method, device, equipment and storage medium - Google Patents
Load adjustment method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a load adjustment method, a load adjustment device, equipment and a storage medium, which are used for quickly and accurately reducing the load fluctuation of the equipment, improving the flexibility of load adjustment, improving the customer satisfaction and reducing the manual analysis cost. The load adjusting method comprises the following steps: predicting a load expected value of the network equipment at a set time, and sampling to obtain an actual load value of the network equipment at the set time; determining that the network equipment needs to carry out load adjustment according to the actual load value and the load expected value; determining respective correlation coefficients of various load influencing factors of the network equipment, and selecting a primary load influencing factor from the various load influencing factors according to the respective correlation coefficients of the various load influencing factors; and adjusting the load of the network equipment according to the primary load influence factor.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a load adjustment method, apparatus, device, and storage medium.
Background
NB-IOT (Narrow Band Internet of Things) focuses on low power consumption and wide coverage of the Internet of Things market, and is an emerging technology which can be widely applied in the global scope. Compared with 2\3\4G, one NB-IOT cell can support 10 ten thousand connections and can provide 50-100 times of access number in the prior wireless technology.
Under the condition that a large number of connections exist on a wireless side, a large number of IOT terminals under an industry user may initiate services at the same time in a short time and access the services to a mobile network in a centralized manner, which very easily causes NB-IOT EPC (Evolved Packet Core network) devices (such as MME (Mobility Management Entity) \\ SGW (Serving GateWay) congestion and even entire network downtime).
In an LTE (Long Term Evolution) -EPC/IOT-EPC converged networking phase, a whole core side device (e.g., MME \ SGW) is shared by two wireless networks (4G LTE and NB-IOT internet of things). The factors causing the load fluctuation of the core-side equipment are mainly as follows: NB-IOT (Internet of things) terminal behavior, 4G LTE (Long term evolution) user behavior, self-bearing capacity of equipment and the like. The MME is mainly responsible for ciphering, integrity protection, and security control of non-access stratum signaling, and performs mobility management on the mobile station in an idle state (to be simply referred to as related work of the control plane). The S-GW is mainly responsible for user plane packet and user plane switching (to be simply referred to as user plane related work). In an LTE-EPC/IOT-EPC fusion networking scene, an MME receives and processes signaling messages of an LTE base station and an NB-IOT base station, cannot identify whether a newly accessed LTE user or an NB-IOT terminal is accessed, and cannot sense the influence of the newly accessed user or terminal on the running state and the bearing capacity of equipment at any time.
In the prior art, for IOT-EPC, a method for reducing load fluctuation is as follows: the method comprises the steps of configuring a request threshold in advance for a certain specific service, judging a requested terminal to be an abnormal terminal when the request frequency of the service is greater than or equal to the configured threshold within a specific time, and refusing the access of the terminal so as to achieve the purposes of congestion control and load reduction. The mode of reducing the load can only manually configure the congestion control function in advance for the NB-IOT service which is easy to generate high signaling flow in a short time through a maintenance person, and can process the known high signaling load in advance, but can not solve the sudden signaling storm.
For LTE-EPC, the method for reducing load fluctuation comprises the following steps: the user distribution ratio and weight of the device are continuously adjusted, or the user is manually migrated to other devices through commands. There is no solution to the problem of load fluctuation in the converged networking environment. The method mainly achieves the purpose of reducing the load of the equipment by manually transferring the user staying on the high-load equipment to other equipment, and has very low efficiency and accuracy, particularly under the condition of converged networking.
In conclusion, under the LTE-EPC/IOT-EPC fusion networking scene, how to quickly and accurately reduce the equipment load fluctuation is a problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a load adjustment method, a load adjustment device, equipment and a storage medium, which are used for quickly and accurately reducing the load fluctuation of the equipment, improving the flexibility of load adjustment, improving the customer satisfaction degree and reducing the manual analysis cost.
In a first aspect, an embodiment of the present invention provides a load adjustment method, where the load adjustment method includes:
predicting a load expected value of the network equipment at a set time, and sampling to obtain an actual load value of the network equipment at the set time;
determining that the network equipment needs to carry out load adjustment according to the actual load value and the load expected value;
determining respective correlation coefficients of various load influencing factors of the network equipment, and selecting a primary load influencing factor from the various load influencing factors according to the respective correlation coefficients of the various load influencing factors;
and adjusting the load of the network equipment according to the primary load influence factor.
In a possible embodiment, predicting the load expected value of the network device at the set time includes:
acquiring recorded load sampling values of the network equipment in continuous n sampling periods before the set time, wherein n is an integer greater than 1;
and calculating the average value of the acquired n load sampling values, and taking the average value as the load expected value of the network equipment at the set time.
In a possible embodiment, the determining that the network device needs to perform load adjustment according to the actual load value and the load expected value includes:
and calculating an absolute value of a difference value between the actual load value and the load expected value, and if the absolute value of the difference value is determined to be greater than or equal to a preset threshold value, determining that the network equipment needs to perform load adjustment.
In a possible embodiment, determining the respective correlation coefficients of the load influencing factors of the network device includes:
and for each of the load influence factors, determining a correlation coefficient of the load influence factor according to the values of the load influence factors of the network equipment obtained by sampling at different time points and corresponding load values.
In a possible embodiment, selecting a primary load influencing factor from the load influencing factors according to the respective correlation coefficients of the load influencing factors includes:
selecting load influence factors of which the absolute values of the correlation coefficients are larger than a set value from the various load influence factors, and taking the selected load influence factors as alternative factors;
and selecting the load influence factor with the maximum absolute value of the correlation coefficient from the candidate factors, and determining the load influence factor with the maximum absolute value of the correlation coefficient as the primary load influence factor.
In a possible implementation manner, if the primary load influencing factor is a narrowband internet of things terminal signaling flow, the adjusting the load of the network device according to the primary load influencing factor includes:
controlling the flow of the signaling flow generated by the narrow-band Internet of things terminal; and/or the presence of a gas in the gas,
determining a cell based on a cell global identifier (ECGI), and paging the narrow-band Internet of things terminal in the determined cell; and/or the presence of a gas in the gas,
the periodic location update timer of the core network is cancelled.
In a possible embodiment, if the primary load influencing factor is the number of mobile communication users, the adjusting the load of the network device according to the primary load influencing factor includes:
determining an average value of the number of users of each device in a pool of a mobile communication system, and selecting a device with the number of users higher than the average value and the largest difference value with the average value from the pool;
and determining the number of the users of the selected equipment which need to be moved according to the number of the users of the selected equipment and the average value.
In a possible embodiment, if the primary load influencing factor is an alarm amount, the adjusting the load of the network device according to the primary load influencing factor includes:
and sequentially processing the faults according to the sequence of the alarm levels from high to low.
In a second aspect, an embodiment of the present invention provides a load adjustment apparatus, including:
the first processing module is used for predicting the load expected value of the network equipment at the set time and sampling to obtain the actual load value of the network equipment at the set time;
the second processing module is used for determining that the network equipment needs to carry out load adjustment according to the actual load value and the load expected value;
a third processing module, configured to determine respective correlation coefficients of each load influencing factor of the network device, and select a primary load influencing factor from the load influencing factors according to the respective correlation coefficients of each load influencing factor;
and the fourth processing module is used for adjusting the load of the network equipment according to the primary load influence factor.
In a third aspect, an embodiment of the present invention provides a load adjustment device, including a processor and a memory, where a preset program is stored in the memory, and the processor reads the program in the memory and executes the load adjustment method according to the program.
In a fourth aspect, an embodiment of the present invention provides a storage medium, where a computer program is stored in the storage medium, and the computer program is used for executing the load adjustment method according to the computer program after being loaded by a processor.
Based on the above technical solutions, in the embodiments of the present invention, whether the network device needs to perform load adjustment is determined according to a load expected value and an actual load value of the network device at a set time, and after it is determined that the network device needs to perform load adjustment, respective correlation coefficients of each load influencing factor of the network device are determined, and then a primary load influencing factor is selected from the load influencing factors, and the load of the network device is adjusted according to the primary load influencing factor, so that the network device can timely know whether load adjustment needs to be performed by monitoring a load condition, find the primary influencing factor in a condition that load adjustment is needed, perform targeted adjustment, achieve dynamic adjustment of the network load, enhance an automatic adjustment capability of the network device in the face of sudden impact, and perform targeted adjustment, the blind operation of unknown reasons is avoided. In conclusion, the embodiment of the invention realizes the purposes of quickly and accurately reducing the load fluctuation of equipment, improving the flexibility of load adjustment, improving the customer satisfaction degree and reducing the manual analysis cost.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart illustrating a method for load adjustment according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a load leveling apparatus according to an embodiment of the present invention;
fig. 3 shows a schematic structural diagram of a load adjustment apparatus in an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In order to quickly and accurately reduce the load fluctuation of the device, improve the flexibility of load adjustment, improve the customer satisfaction degree, and reduce the manual analysis cost, the embodiment of the present invention provides a load adjustment method, which may be applied to a core network device, such as an MME. In the following embodiments, the application to the MME is merely exemplified for specific description.
As shown in fig. 1, the method is implemented as follows:
step 101: and predicting the load expected value of the network equipment at the set time, and sampling to obtain the actual load value of the network equipment at the set time.
In one embodiment, the process of predicting the load expectation of the network device at the set time includes: acquiring load sampling values of recorded network equipment in continuous n sampling periods before set time, wherein n is an integer greater than 1; and calculating the average value of the acquired n load sampling values, and taking the average value as the load expected value of the network equipment at the set time.
For example, taking a sampling period of one day (24 hours) as an example, suppose that the load expectation value of the network device is represented by e (X), suppose X1、X2、X3、……、XnUsing the obtained load sample values of the network device, p (X), at the same time point for each day of the previous n days1),p(X2),p(X3)，……p(Xn) For each of the n load sample values a corresponding probability function is assumed, where each probability function is 1/n. Then:
it should be noted that, the sampling period is only illustrated as 24 hours, and in practical applications, the sampling period may be set to other values as needed, which is merely an example and is not used to limit the protection scope of the embodiment of the present invention.
Step 102: and determining that the network equipment needs to be subjected to load adjustment according to the actual load value and the expected load value.
Specifically, an absolute value of a difference between the actual load value and the expected load value is calculated, and if the absolute value of the difference is determined to be greater than or equal to a preset threshold value, it is determined that the network device needs to perform load adjustment.
Assuming that the actual load value of the network device obtained by sampling at the set time is denoted as e (x)', and the preset threshold value is denoted as α, the condition that the network device needs to perform load adjustment is determined to be:
|E(X)-E(X)’|≥α。
step 103: determining respective correlation coefficients of various load influencing factors of the network equipment, and selecting the primary load influencing factor from the various load influencing factors according to the respective correlation coefficients of the various load influencing factors.
In the embodiment of the invention, the statistical simple correlation coefficient (also called Pearson correlation coefficient) can be used for determining the closeness degree of the relation between the load influence factors and the abnormal equipment load, namely the correlation coefficient.
In one embodiment, for each of the load influencing factors, the specific process of determining the correlation coefficient of the load influencing factor is as follows: and determining the correlation coefficient of the load influence factor according to the values of the load influence factor of the network equipment obtained by sampling at different time points and the corresponding load values. The respective correlation coefficient for each load influencing factor can be determined in the same manner.
Specifically, assuming that the correlation coefficient is represented as r, the correlation coefficient can be calculated by the following formula:
wherein x isiA measured value, y, representing the influencing factor measured at the ith time pointiRepresenting the measured load value of the network device at the ith point in time,
represents the average of the measured values of the influencing factors measured at the respective points in time,
which represents the average of the load values of the network device measured at the respective points in time.
The value of the correlation coefficient fluctuates in the range of-1 to +1, the closer the absolute value of the correlation coefficient is to 1, the closer the relationship between the load influence factor corresponding to the correlation coefficient and the equipment load is, and on the contrary, the closer the absolute value of the correlation coefficient is to 0, the closer the relationship between the load influence factor corresponding to the correlation coefficient and the equipment load is.
It should be noted that, in the embodiment of the present invention, only one determination method of the correlation coefficient is provided, but this does not mean that only the determination method of the correlation coefficient may be adopted in the embodiment of the present invention, and other determination methods of the correlation coefficient may also be applied to the embodiment of the present invention, and the scope of the embodiment of the present invention is not limited thereto.
In one embodiment, the primary load influencing factors are determined by: selecting load influence factors of which the absolute values of the correlation coefficients are larger than a set value from the various load influence factors, and taking the selected load influence factors as alternative factors; and selecting the load influence factor with the maximum absolute value of the correlation coefficient from the candidate factors, and determining the load influence factor with the maximum absolute value of the correlation coefficient as a primary load influence factor.
Specifically, assuming that the absolute value of the correlation coefficient is greater than 0.7, which indicates that the degree of correlation between the load influencing factor corresponding to the correlation coefficient and the network device load is high, the load influencing factor is listed as one of the candidate factors. And taking the load influence factor with the maximum absolute value of the correlation coefficient in the alternative factors as a primary load influence factor, namely the primary reason influencing the load of the network equipment.
For example, a first load influence factor, analysis of correlation between a narrowband internet of things terminal signaling flow and a network device load: collecting the number (namely x) of narrow-band internet-of-things terminals sending signaling requests and collecting the load value (namely y) of network equipment at the same time point by taking hours as granularity, calculating according to a calculation formula of a correlation coefficient to obtain the correlation coefficient, and when the absolute value of the correlation coefficient is greater than 0.7, listing the load influence factor as a candidate factor, otherwise, not considering the load influence factor;
load influence factor two, the correlation analysis of the number of LTE users and the network equipment load: collecting LTE user data (namely x) residing in the network equipment by taking hours as granularity, collecting a load value (namely y) of the network equipment at the same time point, calculating according to a calculation formula of a correlation coefficient to obtain the correlation coefficient, and when the absolute value of the correlation coefficient is greater than 0.7, listing the load influence factor as a candidate factor, otherwise, not considering the load influence factor;
load influence factor three, the correlation analysis of the alarm amount and the network equipment load: collecting alarm amount (namely x) generated by the network equipment by taking hours as granularity, collecting load value (namely y) of the network equipment at the same time point, calculating according to a calculation formula of a correlation coefficient to obtain the correlation coefficient, and when the absolute value of the correlation coefficient is more than 0.7, listing the load influence factor as a candidate factor, otherwise, not considering the load influence factor;
from the alternative factors, the load influencing factor with the largest absolute value of the correlation coefficient is selected as the primary reason for the high load of the network device.
Step 104: the load of the network device is adjusted according to the primary load influencing factor.
In the embodiment of the invention, the load of the network equipment is adjusted in different modes according to different primary load influence factors. The load adjustment process corresponding to different primary load influencing factors is exemplified by listing A, B, C.
A. If the primary load influence factor is a narrowband internet of things terminal signaling flow, the manner of adjusting the load of the network device may be any one or a combination of more than three manners, preferably, when the three manners are implemented in combination, the optimal implementation sequence sequentially includes a manner one, a manner two, and a manner three from front to back, specifically as follows:
the method is characterized in that flow control is carried out on the signaling flow generated by the narrow-band internet of things terminal, namely, the generated signaling flow is subjected to buffer control.
Because the time delay sensitivity of the narrow-band internet of things terminal is much lower than that of the LTE terminal, the MME groups the received signaling messages, divides the conversational messages with high service transmission dependence degree into service message groups, and divides the mobile messages with low service transmission degree into non-service message groups. The mobility message may be an Attach Request (Attach Request) message, a Tracking Area Update (TAU) Request message, a Detach (Detach) Request message, or the like. The mobility message provides a location information service for the narrowband internet of things terminal, and the conversational message (a message except the mobility message) is used for carrying out bearer management and data transmission service. The flow control may be performed by preferentially processing messages in M service message groups and then processing messages in N non-service message groups, where M is greater than or equal to 5N. Messages within the same group are managed according to the queue principle, i.e. first-in first-out, batch processing.
It should be noted that, after performing flow control, the terminal may delay receiving an original signaling message, and if a timer on the terminal side is overtime, which causes message retransmission, a larger signaling storm may be brought to the network, and to avoid this result, after starting flow control, the MME sends a timer update message to the terminal side to extend the timing duration of the timer, for example, if the original timing duration of the timer is P, the duration of the timer after delay is Q, and Q is greater than or equal to 5P.
And secondly, determining a Cell based on an E-UTRAN Cell Global Identifier (ECGI), and paging the narrowband Internet of things terminal in the determined Cell, namely, implementing accurate paging. The method is implemented after the first mode, and the triggering area of the signaling after flow control can be shrunk to reduce secondary disasters.
Because most narrow-band Internet of things terminals are in a static state, mobility requirements are much lower than those of LTE users, a large amount of network signaling is consumed in a mode of paging the narrow-band Internet of things terminals through tracking a region code (TAC) region, in view of the above, the paging mode of the narrow-band Internet of things terminals is changed from the TAC region-based paging mode to the mode of preferentially paging a single cell according to ECGI (evolved packet access gateway) stored in an MME (mobility management entity), after paging fails, the paging mode is changed to the mode of paging through the TAC region, and after the corresponding cell is paged through the TAC region, ECGI cell identifiers of the corresponding narrow-band Internet of things terminals stored in the MME are updated, so that the narrow-band physical network terminal can be paged accurately next time. By adopting the method, the network paging signaling overhead can be reduced by 99%.
And thirdly, canceling the periodic location update timer of the core network, wherein the periodic location update timer comprises a Routing Area Update (RAU) timer and/or a Tracking Area Update (TAU) timer. This approach may avoid generating unnecessary signaling overhead.
When the network is impacted by a large amount of signaling, the MME core network side cancels a periodic location updating timer (RAU/TAU) to reduce the number of awakening times of non-service use of the narrowband Internet of things terminal until determining that the location does not need to be adjusted, namely determining that the load value of the network equipment acquired at the current time point deviates from the load expected value at the current time point and is less than a preset threshold, and recovering the periodic location updating timer.
B. If the primary load influence factor is the number of mobile communication subscribers, the method for adjusting the load of the network device is as follows: determining the average value of the number of users of each device in a pool group of the mobile communication system, and selecting the device with the number of users higher than the average value and the largest difference value with the average value from the pool group; and determining the number of the users of the selected equipment needing to be moved according to the number of the users of the selected equipment and the average value.
Using an LTE pool as an example, the variance of the number of users (denoted S) for multiple devices within the LTE pool is used2) To determine whether the number of users of a single device in the pool has risen or the number of users of multiple devices in the pool has risen. If the variance is smaller than the set value, the number of users of a plurality of devices in the pool is increased, and the capacity expansion of a part needs to be maintained due to the increase of the number of the users; if the variance is larger than the set value, the user data of the individual device is increased, and the load of the device needs to be reduced by means of transferring users. The calculation formula of the user number variance is as follows:
wherein x is1......xnRepresenting the number of users of n devices in the pool measured at a certain point in time,
representing the average of the number of users of n devices in the pool.
And counting the number of the users of n devices in the unified pool group at a time point, if the number of the users of the devices deviates from the average value to the maximum, the users of the devices need to be moved to other devices according to a proportion, and if the devices are not the devices with the number of the users deviating from the average value to the maximum, the users of the devices are not moved temporarily.
The number of users to be moved by the equipment is equal to the average value of the number of users registered by the equipment in the pool minus the number of users registered by each equipment in the pool. The proportion of users that the device needs to be relocated is equal to the number of users that need to be relocated divided by the number of users registered by the device.
C. If the primary load influence factor is the alarm amount, the method for adjusting the load of the network equipment is as follows: and sequentially processing the faults according to the sequence of the alarm levels from high to low.
If the load of the equipment is increased and closely related to the fault of the equipment, the load is reduced by processing the fault preferentially, and when the fault is processed, the processing is carried out according to the alarm level, namely the order of processing the alarm is as follows: emergency alert > important alert > general alert > prompt alert.
In the embodiment of the present invention, after step 104 is executed, step 101 is executed again, that is, the expected load value of the network device is predicted, and if the absolute value of the difference between the predicted value and the sampled actual load value is smaller than the preset threshold value, the network device load is considered to be normal, and the system is stable; otherwise, continuing to adjust the load according to the load adjusting process.
Generally, according to steps 101 to 104, after the load adjustment procedure is repeatedly executed for 2 to 3 times, the load of the network device can reach a stable state.
Based on the above technical solutions, in the embodiments of the present invention, the difference between the actual load value and the load expected value is used as a basis for measuring whether the device needs to perform load adjustment, rather than using a single sampled load value as a basis, so that the accuracy of the determination is improved, and an operator can dynamically adjust the device load, thereby enhancing the automatic adjustment capability of the network in the face of sudden impact, and realizing the optimal management and control of the network device.
In addition, in the embodiment of the invention, the load value of the network equipment, the number of the users of the LTE, the signaling flow of the narrowband Internet of things terminal, the alarm number and the like are detected once at intervals T1, the load expected value of the network equipment is predicted according to the recorded historical value, and the load deviation state of the network equipment is determined, so that whether the load of the network equipment needs to be adjusted is judged, the primary load influence factors are selected through correlation analysis, and blind operation of unknown reasons is avoided.
In summary, the embodiment of the invention can automatically adjust the load of the equipment, prevent the condition of overhigh load of the equipment, has high implementation efficiency and accuracy, can quickly and accurately reduce the load fluctuation of the equipment, improves the flexibility of load adjustment, greatly improves the maintenance efficiency, improves the customer satisfaction, reduces the workload of maintenance personnel and reduces the manual analysis cost.
Based on the same inventive concept, the embodiment of the present invention further provides a load adjusting apparatus, and the specific implementation of the apparatus may refer to the description of the method embodiment, and repeated descriptions are omitted, as shown in fig. 2, the apparatus mainly includes:
the first processing module 201 is configured to predict a load expected value of a network device at a set time, and sample to obtain an actual load value of the network device at the set time;
a second processing module 202, configured to determine that the network device needs to perform load adjustment according to the actual load value and the expected load value;
a third processing module 203, configured to determine respective correlation coefficients of various load influencing factors of the network device, and select a primary load influencing factor from the various load influencing factors according to the respective correlation coefficients of the various load influencing factors;
a fourth processing module 204, configured to adjust the load of the network device according to the primary load influencing factor.
Based on the same inventive concept, an embodiment of the present invention further provides a load adjustment device, and specific implementation of the device may refer to descriptions in the method embodiment, and repeated parts are not repeated, as shown in fig. 3, the device mainly includes a processor 301 and a memory 302, a preset program is stored in the memory, the processor reads the program in the memory, and the following processes are performed according to the program:
predicting a load expected value of the network equipment at a set time, and sampling to obtain an actual load value of the network equipment at the set time;
determining that the network equipment needs to carry out load adjustment according to the actual load value and the load expected value;
determining respective correlation coefficients of various load influencing factors of the network equipment, and selecting a primary load influencing factor from the various load influencing factors according to the respective correlation coefficients of the various load influencing factors;
and adjusting the load of the network equipment according to the primary load influence factor.
In particular implementations, the memory and the processor are connected in a bus, which may include any number of interconnected buses and bridges that link together various circuits including one or more processors represented by the processors and memory represented by the memory. The bus may also link various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory may be used to store data used by the processor in performing operations.
Alternatively, the processor may be a CPU (central processing unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a PLD (Complex Programmable Logic Device).
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method of load adjustment, comprising:
predicting a load expected value of the network equipment at a set time, and sampling to obtain an actual load value of the network equipment at the set time;
determining that the network equipment needs to carry out load adjustment according to the actual load value and the load expected value;
determining respective correlation coefficients of various load influencing factors of the network equipment, and selecting a primary load influencing factor from the various load influencing factors according to the respective correlation coefficients of the various load influencing factors;
adjusting the load of the network equipment according to the primary load influence factor;
wherein the determining of the respective correlation coefficients of the load influencing factors of the network device includes: and for each of the load influence factors, determining a correlation coefficient of the load influence factor according to the values of the load influence factors of the network equipment obtained by sampling at different time points and corresponding load values.
2. The method of claim 1, wherein predicting the expected load value of the network device at a set time comprises:
acquiring recorded load sampling values of the network equipment in continuous n sampling periods before the set time, wherein n is an integer greater than 1;
and calculating the average value of the acquired n load sampling values, and taking the average value as the load expected value of the network equipment at the set time.
3. The method according to claim 1 or 2, wherein the determining that the network device needs to perform load adjustment according to the actual load value and the expected load value comprises:
and calculating an absolute value of a difference value between the actual load value and the load expected value, and if the absolute value of the difference value is determined to be greater than or equal to a preset threshold value, determining that the network equipment needs to perform load adjustment.
4. The method according to claim 1 or 2, wherein selecting a primary load influencing factor from the load influencing factors according to the correlation coefficients of the load influencing factors comprises:
selecting load influence factors of which the absolute values of the correlation coefficients are larger than a set value from the various load influence factors, and taking the selected load influence factors as alternative factors;
and selecting the load influence factor with the maximum absolute value of the correlation coefficient from the candidate factors, and determining the load influence factor with the maximum absolute value of the correlation coefficient as the primary load influence factor.
5. The method according to claim 1 or 2, wherein if the primary load influencing factor is a narrowband internet of things terminal signaling flow, the adjusting the load of the network device according to the primary load influencing factor includes:
controlling the flow of the signaling flow generated by the narrow-band Internet of things terminal; and/or the presence of a gas in the gas,
determining a cell based on a cell global identifier (ECGI), and paging the narrow-band Internet of things terminal in the determined cell; and/or the presence of a gas in the gas,
the periodic location update timer of the core network is cancelled.
6. The method according to claim 1 or 2, wherein if the primary load influencing factor is the number of mobile communication subscribers, the adjusting the load of the network device according to the primary load influencing factor comprises:
determining an average value of the number of users of each device in a pool of a mobile communication system, and selecting a device with the number of users higher than the average value and the largest difference value with the average value from the pool;
and determining the number of the users of the selected equipment which need to be moved according to the number of the users of the selected equipment and the average value.
7. The method according to claim 1 or 2, wherein if the primary load influencing factor is an alarm amount, the adjusting the load of the network device according to the primary load influencing factor comprises:
and sequentially processing the faults according to the sequence of the alarm levels from high to low.
8. A load leveling device, comprising:
the first processing module is used for predicting the load expected value of the network equipment at the set time and sampling to obtain the actual load value of the network equipment at the set time;
the second processing module is used for determining that the network equipment needs to carry out load adjustment according to the actual load value and the load expected value;
a third processing module, configured to determine respective correlation coefficients of each load influencing factor of the network device, and select a primary load influencing factor from the load influencing factors according to the respective correlation coefficients of each load influencing factor;
the fourth processing module is used for adjusting the load of the network equipment according to the primary load influence factor;
the third processing module is configured to determine a correlation coefficient of each load influencing factor of the network device, and includes: the third processing module is configured to, for each of the load influencing factors, determine a correlation coefficient of the load influencing factor according to a value of the load influencing factor of the network device obtained by sampling at different time points and a corresponding load value.
9. A load adjusting apparatus comprising a processor and a memory, wherein a preset program is stored in the memory, and the processor reads the program in the memory and executes the load adjusting method according to any one of claims 1 to 7.
10. A storage medium having stored therein a computer program for executing the load adjustment method according to any one of claims 1 to 7, when the computer program is loaded by a processor.
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| CN112996041B (en) * | 2019-12-02 | 2022-10-18 | 中国移动通信集团安徽有限公司 | Flow control method, device and equipment |
| CN113133042A (en) * | 2019-12-30 | 2021-07-16 | 中国移动通信集团四川有限公司 | Load fluctuation adjusting method, device, equipment and storage medium |
| CN114615698B (en) * | 2020-12-09 | 2023-07-18 | 中国移动通信集团四川有限公司 | IBCF interworking gateway load adjustment method and device |
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