System and method for optimizing the impact of service outages by minimizing active users within a network

Abstract

Provided are a system and method for optimizing the impact of service outages by minimizing active users within a network. The present disclosure provides a system and method for optimizing the impact of service outages in a network. The system receives at least one data packet including one or more parameters from one or more computing devices associated with one or more active users within at least one cell. The one or more parameters include network key performance indicators (KPIs), alarms, and faults. Further, the system analyzes the data packet to identify one or more patterns in a time series and correlates them with the one or more parameters. Finally, the system predicts the lean time of one or more computing devices and changes the configuration of the one or more computing devices during the lean time to minimize active users and optimize the impact of service outages in the network.

Description

Technical Field

[0001] Reservation of Rights Part of the disclosure of this patent document includes materials that are the subject of intellectual property rights owned by Jio Platforms Limited (JPL) or its related companies (hereinafter collectively referred to as the patent holders), such as, but not limited to, copyrights, designs, trademarks, integrated circuit (IC) layout designs, and / or trade dress protection. The patent holders do not object to third parties copying the patent documents or patent disclosures described in the patent files or records of the Patent and Trademark Office, but reserve all other rights. All rights to such intellectual property are fully reserved by the patent holders.

[0002] Embodiments of the present disclosure generally relate to the deployment of telecommunications. More specifically, the present disclosure relates to systems and methods for optimizing the impact of service outages by minimizing active users within a network.

Background Art

[0003] The following description of related art is intended to provide background information related to the field of the present disclosure. This section may include specific aspects of the technology that may be related to various features of the present disclosure. However, this section is only intended to deepen the reader's understanding of the present disclosure and does not admit prior art.

[0004] The establishment of the Global System for Mobile Communications Association (GSMA) has opened the way for the standardization of wireless communications and the easy access to and adaptation of core technical knowledge, leading to improved adaptability and increased demand. The improvement of chip design has also promoted the standardization of wireless communications, achieving reduced power consumption and improved integration. Furthermore, the reduction of power consumption and the high density of components have increased the processing speed, enabling engineers to develop new features that were previously considered impossible due to hardware limitations. Additionally, while new features and software releases are usually implemented every four to five years, in modern society today, software releases and features are being introduced to the network every three to four months. Restarting the communication nodes is required for all new releases, interrupting the services provided to end-users. Separately, many user-configurable parameters need the nodes to be restarted to be effective when changed, and when such parameter values are optimized by the operation team, it can also cause service interruptions.

[0005] To provide the best service experience to end-users, communication networks are constantly being expanded, upgraded, and optimized. All these activities include changes to the configuration of existing nodes, addition of new nodes to meet increased demand, software and firmware upgrades, and deployment of new features in the form of changes to parameter values. In most cases, changes to the configuration result in the interruption of existing services for a certain period. Service interruptions occur to all users (active users) using the network at that time.

[0006] However, in existing systems that allow for configuration changes, network operators need to respond quickly to problems, investigate the cause of the disruption, and perform operations to restore normal network operations and minimize the impact on active users. Network operators have adopted various methods to minimize the number of customers experiencing this service disruption by deploying changes to the new software configuration assuming low active user numbers at night. This reduces the impact of the service disruption, but as demonstrated in the case study of Mumbai, India, it can be further improved significantly.

[0007] Therefore, there is a need in the art to provide systems and methods that can mitigate problems associated with the prior art.

[0008] Objectives of the present disclosure Some of the objectives of the present disclosure that are met by at least one embodiment of this specification are listed below.

[0009] An object of the present invention is to provide a system and method that optimize the impact of service outages by minimizing active users within the network.

[0010] An object of the present disclosure is to provide a system and method of an enhanced mechanism for performing configuration changes within a network by enabling changes to the lean time of individual cells and minimizing the number of active users experiencing service disruptions.

[0011] An object of the present invention is to provide a system and method that achieve user scalability and flexibility according to the requirements of individual active users during configuration changes.

[0012] An object of the present disclosure is to provide a system and method that minimize the temporary interruption of services and prevent the user experience from being interrupted or degraded due to users encountering data packet loss, errors occurring, or data transmission becoming incomplete.

[0013] An object of the present disclosure is to provide a system and method for scheduling configuration changes that affect a service during off-peak hours or low-demand periods in order to minimize the number of active users affected.

[0014] An object of the present invention is to provide a system and method that improve user satisfaction by minimizing the number of users affected by service interruptions and maintaining user satisfaction at a higher level.

[0015] An object of the present invention is to provide a system and method for performing configuration changes that affect a service during lean times while providing the network administrator with a less congested and more manageable environment for implementing the changes.

[0016] An object of the present disclosure is to provide a system and method that promote a smoother implementation process, reduce the risk of errors, and improve overall efficiency. SUMMARY OF THE INVENTION PROBLEMS TO BE SOLVED BY THE INVENTION

[0017] In this section, specific objects and aspects of the present disclosure, which will be described in detail in subsequent sections, are briefly explained. This summary is not intended to identify key features or circumscribe the scope of the claimed subject matter.

[0018] In one aspect, the present disclosure relates to a system for optimizing the impact of service outages in a network. The system includes one or more processors and a memory operably coupled to the one or more processors, with instructions stored in the memory that, when executed, cause the one or more processors to receive at least one data packet including one or more parameters from one or more computing devices associated with one or more active users within at least one cell. Further, the one or more processors are configured to analyze at least one data packet to identify one or more patterns in a time series and correlate with the one or more parameters. Additionally, the one or more processors are configured to predict a lean time of the one or more computing devices based on a time series prediction of the at least one data packet. Finally, the one or more processors are configured to change the configuration of the one or more computing devices during the lean time to minimize one or more active users and optimize the impact of service outages in the network.

[0019] In one embodiment, the one or more parameters may include at least one of network key performance indicators (KPIs), alarms, and faults.

[0020] In one embodiment, the one or more patterns may include at least one of trends, seasonality, white noise, and random noise.

[0021] In one embodiment, the one or more processors may be configured to receive at least one data packet from the one or more computing devices based on a predetermined time. The predetermined time may be related to the aggregation of at least one data packet within bins of one hour each day.

[0022] In one embodiment, one or more processors may be configured to check whether the state of one or more computing devices corresponds to lean time. Further, one or more processors may be configured to schedule the activity time of one or more computing devices based on the lean time. The activity time may be related to the execution of a network service impact configuration change operation in one or more computing devices.

[0023] In one embodiment, the network service impact configuration change operation may include at least one of a firmware upgrade, a reconfiguration of network devices, a migration of network services, and an optimization of network capacity.

[0024] In one embodiment, one or more processors may be configured to sort at least one data packet, eliminate one or more outliers, and obtain a time series prediction based on predicting the lean time of one or more computing devices.

[0025] In one embodiment, the lean time of one or more computing devices may be predicted based on one or more categories of one or more active users of one or more computing devices. The one or more categories may include at least one of a low load category, a medium load category, and a high load category.

[0026] In one embodiment, the time series is related to a set of one or more observed values, and each observed value is associated with a specific time index.

[0027] In one aspect, the present disclosure relates to a method for optimizing the impact of service outages in a network. The method includes receiving, by one or more processors, at least one data packet including one or more parameters from one or more computing devices associated with one or more active users within at least one cell. The method includes analyzing, by one or more processors, at least one data packet to identify one or more patterns in a time series and correlating them with one or more parameters. The method includes predicting, by one or more processors, a lean time of one or more computing devices based on a time series prediction. Finally, the method includes performing, by one or more processors, a change in the configuration of one or more computing devices during the lean time by minimizing one or more active users and optimizing the impact of service outages in the network.

[0028] In one aspect, the present disclosure relates to a user equipment (UE). The UE includes one or more processors coupled to a memory, and instructions are stored in the memory, which, when executed, cause the one or more processors to identify one or more temporary service interruptions during a configuration process. The one or more processors are configured to transmit at least one data packet including one or more parameters to a system within a cell. The one or more parameters may include at least one of network KPIs, alarms, and faults. The one or more processors are configured to receive and execute one or more instructions from the system at a predicted lean time based on a time series prediction to optimize the impact of service outages in the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosed methods and systems, and like reference numerals refer to like parts throughout the different views. The components in the drawings are not necessarily to scale, emphasis having been placed instead upon clearly illustrating the principles of the disclosure. In some of the drawings, block diagrams are used to show components and may not represent the internal circuitry of each component. Those skilled in the art will appreciate that the disclosure of such drawings of the invention includes the disclosure of the electrical, electronic, or circuit components commonly used to implement such components.

[0030]

Figure 1

Figure 2

Figure 3

Figure 4A

Figure 4B

Figure 5

DETAILED DESCRIPTION OF THE INVENTION

[0031] For the purposes of the following description, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Some of the features described below may be used independently or in combination with other features. Individually, each feature may not be able to solve all of the problems described above or may only be able to solve some of the problems described above. Some of the problems described above may not be completely solved by any of the features described below.

[0032] The following description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the purpose is to provide an effective explanation for those skilled in the art to implement exemplary embodiments. It should be understood that various changes can be made to the functions and arrangements of the elements without departing from the spirit and scope of the disclosed description.

[0033] The present disclosure provides a robust and effective solution for implementing a system and method that optimize the impact of service outages by minimizing active users within a network. The proposed system incorporates schedule changes for network services, which affect configuration changes during off-peak hours or low-demand periods of one or more computing devices within a cell of the network, minimizing the number of active users affected by the disruption.

[0034] Various embodiments throughout the present disclosure are described in more detail with reference to FIGS. 1-5.

[0035] FIG. 1 shows an exemplary network architecture 100 in which an embodiment of the present invention can be implemented or has been implemented.

[0036] Referring to FIG. 1, an exemplary network architecture 100 is shown in which a system 110 can be implemented, or is implemented, that optimizes the impact of service outages by minimizing active users within the network. As shown, system 110 can receive at least one data packet including one or more parameters from one or more computing devices (106-1, 106-2, ... 106-N) associated with one or more users (102-1, 102-2, ... 102-N) within at least one cell (104-1, 104-2, ... 104-N).

[0037] In one embodiment, system 110 can be communicatively coupled to one or more computing devices (106-1, 106-2, ... 106-N) via a communication network 108. Those skilled in the art will understand that one or more computing devices (106-1, 106-2 ... 106-N) can be individually referred to as computing device 106 and collectively referred to as computing device 106. Similarly, one or more users (102-1, 102-2, ... 102-N) can be individually referred to as user 102 and / or active user 102 and collectively referred to as user 102 and / or active user 102. Similarly, at least one cell (104-1, 104-2, ... 104-N) can be individually referred to as cell 104 and collectively referred to as cell 104. In one embodiment, computing device 106 can also be referred to as a user equipment (UE). Thus, the terms "computing device" and "user equipment (UE)" can be used interchangeably throughout the present disclosure.

[0038] In one exemplary embodiment, computing device 106 can include one or more processing units such as, but not limited to, a data transmission unit, a data management unit, a display unit, and other units, where the other units can include a storage unit, a computing unit, and / or a signal generation unit, among others (but not limited to these).

[0039] In one embodiment, the computing device 106 may transmit at least one data packet including one or more parameters to the system 110 via a point-to-point or point-to-multipoint communication channel or network 108.

[0040] In one embodiment, the computing device 106 may collect, analyze, and share data received from the system 110 via the communication network 108. In one embodiment, the computing device 106 may enable the presentation of information to one or more users 102.

[0041] In one embodiment, the system 110 may receive at least one data packet including one or more parameters from one or more computing devices 106 associated with the active users 102 within the cell 104. The one or more parameters include, but are not limited to, network key performance indicators (KPIs), alarms, faults, etc. The at least one data packet may be received from one or more computing devices 106 based on a predetermined time. In one embodiment, the predetermined time may be related to the aggregation of at least one data packet within a bin for each hour of each day.

[0042] In one embodiment, the system 110 may analyze at least one data packet to identify one or more patterns in a time series and correlate them with one or more parameters. The one or more patterns include, but are not limited to, trends, seasonality, white noise, random noise, etc. The time series represents a set of one or more observations, and each observation is associated with a specific time index.

[0043] In one embodiment, the system 110 may predict the lean time of one or more computing devices 106 based on a time series prediction of the analyzed data. The time series prediction may be performed based on sorting at least one data packet, excluding one or more outliers, and predicting the lean time of one or more computing devices 106. Further, the lean time of one or more computing devices 106 may be predicted based on one or more categories of active users 102 of the one or more computing devices 106. The one or more categories may include at least one of a low load category, a medium load category, and a high load category.

[0044] In another embodiment, the system 110 may check whether the state of one or more computing devices 106 corresponds to the lean time. Further, the system 110 may schedule the activity time of one or more computing devices 106 based on the lean time. The activity time may be related to the execution of a network service impact configuration change operation in one or more computing devices 106. The network service impact configuration change operation includes, but is not limited to, firmware upgrade, network device reconfiguration, network service migration, network capacity optimization, and the like. Finally, the system 110 changes the configuration of one or more computing devices 106 at the lean time to minimize active users 102 and optimize the impact of service interruption in the network 108.

[0045] In one exemplary embodiment, communication network 108 includes, but is not limited to, at least a portion of one or more networks, which have one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or combine one or more messages, packets, signals, waves, voltage or current levels, or combinations thereof. In one exemplary embodiment, communication network 108 includes, but is not limited to, a wireless network, a wired network, the Internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad-hoc network, an infrastructure network, a public switched telephone network (PSTN), a cable network, a cellular network, a satellite network, an optical fiber network, or combinations thereof. In one exemplary embodiment, communication network 108 includes, but is not limited to, a second-generation network, a third-generation network, a fourth-generation network, a fifth-generation network, a 3GPP® network, and a non-3GPP network, among others.

[0046] In one embodiment, one or more computing devices 106 may communicate with the system 110 via a set of executable instructions residing on any operating system. In one embodiment, one or more computing devices 106 include, but are not limited to, any electrical, electronic, electromechanical, or device, such as a cellular phone, smartphone, virtual reality (VR) device, augmented reality (AR) device, laptop, general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or other computing device, or one or more combinations of the foregoing devices, where one or more computing devices 106 include one or more built-in or externally coupled accessories, which include, but are not limited to, visual assistive devices such as cameras, audio assistive, microphones, keyboards, input devices for receiving input from a user 102 such as touchpads, touch-responsive screens, electronic pens, receiving devices for receiving any audio or visual signal of any frequency range, and transmitting devices capable of transmitting any audio or visual signal of any frequency range. It is understood that one or more computing devices 106 are not limited to the foregoing devices and that various other devices may be used.

[0047] FIG. 1 shows exemplary components of a network architecture 100, but in other embodiments of the network architecture 100, the number, type, and arrangement of components may differ from those in FIG. 1 or may include additional features not shown in FIG. 1. Further, or alternatively, functions described herein as being performed by one or more components of the network architecture 100 may be performed by one or more other components of the network architecture 100.

[0048] FIG. 2 shows an exemplary block diagram 200 of a system 110 proposed in accordance with one embodiment of the present disclosure.

[0049] FIG. 2 shows an exemplary representation of a system 110 that optimizes the impact of service outages by minimizing active users within a network, according to one embodiment of the present disclosure, with reference to FIG. 1. In one aspect, system 110 may include one or more processors 202. The one or more processors 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuits, and / or any device that processes data based on operating instructions. Among other functions, the one or more processors 202 may be configured to obtain and execute computer-readable instructions stored in a memory 204 of system 110. The memory 204 may be configured to store one or more computer-readable instructions or routines on a non-transitory computer-readable storage medium, which may be obtained and executed to create or share data packets via a network service. The memory 204 may include any non-transitory storage device, including, for example, volatile memory such as random access memory (RAM), or non-volatile memory such as erasable programmable read-only memory (EPROM), flash memory.

[0050] Referring to FIG. 2, system 110 may include an interface(s) 206. The interface(s) 206 may include various interfaces, such as, for example, data input / output (I / O) devices, storage device interfaces, etc. The interface(s) 206 may facilitate communication between system 110. The interface(s) 206 may also provide a communication path to one or more components of system 110. Examples of such components include, but are not limited to, processing unit(s) / engine(s) 208 and database 210.

[0051] In one embodiment, the processing unit / engine(s) 208 may be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functions of the processing engine(s) 208. In the examples described herein, such a combination of hardware and programming may be implemented in several different ways. For example, the programming of the processing engine(s) 208 may be processor-executable instructions stored on a non-transitory machine-readable storage medium, and the hardware of the processing engine(s) 208 may include processing resources, such as one or more processors, for executing such instructions. In this example, the machine-readable storage medium may store instructions that, when executed by the processing resources, implement the processing engine(s) 208. In such an example, the system 110 may include a machine-readable storage medium for storing instructions and processing resources for executing the instructions, or the machine-readable storage medium may be separate but accessible to the system 110 and the processing resources. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry.

[0052] In one embodiment, the database 210 may include data stored or generated as a result of functions implemented by components of either the processor 202 or the processing engine 208. In one embodiment, the database 210 may be separate from the system 110.

[0053] In one exemplary embodiment, the processing engine 208 may include one or more engines selected from any of the data acquisition engine 212, the machine learning engine 214, the configuration change engine 216, and other engines 218 such as, but not limited to, a data calculation engine, a data analysis engine, a data sorting engine, etc. The processing engine 208 can be dedicated to performing complex schematic processing, but is not limited thereto.

[0054] In one embodiment, the data acquisition unit 212 may receive at least one data packet including one or more parameters from one or more computing devices 106 of FIG. 1. In one embodiment, the one or more parameters may include, but are not limited to, network KPIs, alarms, and faults.

[0055] In one embodiment, the machine learning engine 214 may predict the lean time of the cell 104 by using time series analysis, which may be a machine learning algorithm.

[0056] In one embodiment, the configuration change engine 216 may perform network service impact configuration change operations on one or more computing devices 106.

[0057] It is understood that the components of the system 110 can be flexibly configured to be able to cope with changes.

[0058] FIG. 3 shows an exemplary representation 300 of the architecture of a process block diagram according to an embodiment of the present disclosure.

[0059] As shown in the figure, block 302 may represent active users 102, alarms, KPIs, faults (also referred to as one or more parameters) in the network. The one or more parameters, along with a list of one or more active users 102, may be sent to block 304 to scrub data and perform analysis. Further, in block 304, data scrubbing and analysis may be performed to obtain the cell lean time of the computing devices 106 within at least one cell 104. Further, the data may be stored in the cell lean time database 306. The output from the database 306 may be sent to the change engine 216, where cell-by-cell change requests may be further stored. The change engine 216 may provide parameter change commands to the lean time in cells 1 (104-1), 2 (104-2), and 3 (104-3).

[0060] In one embodiment, parameter changes can be applied to each cell 104 at each respective lean time, such that the number of active users 102 affected by the stops necessary to effectuate the changes is significantly reduced. Mathematically, the number of active subscribers at the lean time of a cell can be expressed as follows. SC1 + SC2 + SC3 + … SCn > SLC1 + SLC2 + SLC3 + … SLCn Here, S is the number of active subscribers within a cell at the time of change. C1, C2, C3 … Cn = cell numbers from 1 to n within the network SL = number of active subscribers at the lean time of a cell.

[0061] In one embodiment, the lean time of each cell can be predicted daily, and the application of changes can occur at the lean time of the cell.

[0062] In one embodiment, a time series prediction given by {Xt}, t ∈ T can be used to predict the lean time with reasonable (>90% accuracy).

[0063] In one embodiment, a "time series" can be a set of observations indexed by time. Each observation occurs at a certain time t, where t belongs to the set of allowed times T, where T can be discrete, in which case there is a discrete time series, or continuous, in which case there is a continuous time series. Further, one observation of the time series {X t} can be referred to as an actualization of that series. A time series can be composed of distinct patterns including, but not limited to, trends, seasonality, white noise, random noise, etc.

[0064] In one embodiment, a trend may refer to the slope in a time series domain. For example, if the data is generally on an upward trend over a specific period, there may be a scenario where the data is on a downward trend. A trending series may not usually be stationary as the mean changes over time. Processing of trends includes elimination by differencing or use of a backshift operator. In one embodiment, seasonality may refer to a pattern that repeats at weekly, yearly, or other regular intervals. Seasonality may represent a distinct repeating change in a time series. Fitting of seasonality can be done using harmonic regression, for example, fitting the series using many sines and cosines (simplified). Thus, by using different time series models, system 110 may predict the lean time count for each cell. Further, system 110 may predict the lean time count for each cell based on history.

[0065] FIG. 4A shows an exemplary flowchart 400 of a method proposed in accordance with an embodiment of the present disclosure.

[0066] As shown, method 400 may include, at step 402, collecting network KPIs, alarms, and faults from all nodes in the network in one-hour bins every day so that sufficient data is available to perform the correlation. At step 404, method 400 may include performing an analysis of the data to find trends, seasonality, white noise, random noise, etc., and correlating with alarms and faults of neighboring nodes. At step 406, method 400 may include sorting the data, removing outliers due to alarms or faults, and then performing a TME series prediction on the data to predict the lean time for each node. Further, at step 408, method 400 may include checking whether there is a routine service that affects the configuration being executed. If it is lean time, the method may include, at step 412, performing a change in the schedule configuration at each node. If it is not lean time, at step 410, method 400 may include first scheduling activity time to the node lean time before proceeding to step 412.

[0067] Figure 4B shows an exemplary representation 450 of the active user load within a network at different times of the day, according to an embodiment of the present disclosure.

[0068] Figure 4B shows an example of a proposed mechanism for optimizing service outages based on geography, where different numbers of active users 102 can be loaded at different times in different cells, and shows how the proposed method 400 performs a change in the lean time of each cell to achieve service outages caused by the minimum number of active users. The lean time of one or more computing devices 106 can be predicted based on one or more categories of active users 102 of one or more computing devices 106, and the one or more categories can include at least one of a low load category, a medium load category, and a high load category.

[0069] In one embodiment, the system 110 receives one or more parameters from one or more computing devices 106 based on a predetermined time that may be related to the aggregation of at least one data packet in a bin every hour, daily. For example, at time 1, cell 1 and cell 4 may be classified into the low load category. Cells 2, 3, 5, and cell 7 may fall into the medium load category. Cells 6, 8, and 9 may be classified into the high load category. At time 2, cell 2 and cell 5 may be classified into the low load category. Cells 1 and 4 may fall into the medium load category. Cells 3, 6, 7, 8, and 9 may be classified into the high load category. At time 3, cells 2, 3, 6, 7, 8, and 9 may be classified into the low load category. Cells 1, 4, and 5 may fall into the high load category. Therefore, FIG. 4B represents all configuration changes implemented during the lean time.

[0070] As an example, Tables 1 and 2 highlight the observation results during the trial on the circle and the results after network analysis. Table 1 shows the analysis conducted on a specific date, and Table 2 shows the analysis conducted the next day. From the following data, it is clear that each node has a different lean time, and executing network activities at the network lean time may not be an optimal solution.

[0071] [Table 1]

[0072] [Table 2]

[0073] FIG. 5 shows an exemplary computer system 500 in which an embodiment of the present invention can be implemented or in which an embodiment of the present invention is implemented. In one embodiment, a UE such as the UE 106 of FIG. 1 and / or the system 110 proposed in FIG. 1 or FIG. 2 can be implemented as the computer system 500.

[0074] As shown in FIG. 5, computer system 500 may include an external storage device 510, a bus 520, a main memory 530, a read-only memory 540, a mass storage device 550, communication port(s) 560, and a processor 570. Those skilled in the art will understand that computer system 500 may include multiple processors and communication ports. The processor 570 may include various modules related to the embodiments of the present disclosure. The communication port(s) 560 may be an RS-232 port for use in a modem-based dial-up connection, a 10 / 100 Ethernet port, a gigabit or 10 gigabit port using copper wire or optical fiber, a serial port, a parallel port, or any other existing or future port. The communication port(s) 560 may be selected according to the network, or any network to which the computer system 500 is connected. The main memory 530 may be a random access memory (RAM) or other dynamic storage device generally known in the art. The read-only memory 540 may be any static storage device(s). The mass storage device 550 may be any current or future mass storage solution that can be used to store information and / or instructions.

[0075] The bus 520 communicatively couples the processor(s) 570 to other memory, storage, and communication blocks. Optionally, an operator and management interface (display, keyboard, cursor control device, etc.) is also coupled to the bus 520 to support direct interaction between the operator and the computer system 500. Other operator and management interfaces may be provided through a network connection connected via the communication port(s) 560. The above components are only for the purpose of exemplifying various possibilities. The aforementioned exemplary computer system 500 is not intended to limit the scope of the present disclosure in any way.

[0076] Although a great deal of emphasis is placed here on the preferred embodiments, it is understood that many embodiments are possible without departing from the principles of the disclosure, and that many changes can be made to the preferred embodiments. These and other changes to the preferred embodiments of the present disclosure will be apparent to those skilled in the art from the disclosure herein, and it is clearly understood that the foregoing description is illustrative of the present disclosure and not limiting. Advantages of the Present Disclosure

[0077] The present invention provides a system and method for optimizing the impact of service outages by minimizing active users within a network.

[0078] The present invention enables changes to the lean time of individual cells, facilitating an enhanced mechanism for performing configuration changes within a network by minimizing the number of active users experiencing service disruptions.

[0079] The present disclosure facilitates user scalability and flexibility according to the requirements of individual active users during configuration changes.

[0080] The present disclosure minimizes temporary service outages, prevents users from experiencing data packet loss, and prevents errors and incomplete data transmissions, thereby preventing disruptions and degradation of the user experience.

[0081] The present disclosure facilitates scheduling configuration changes that affect the service during off-peak hours or low-demand periods, while minimizing the number of active users affected.

[0082] The present invention contributes to improving user satisfaction by minimizing the number of users affected by service outages and maintaining a higher level of user satisfaction.

[0083] The present disclosure performs configuration changes that affect services during lean times, providing a less congested and more manageable environment for network administrators to implement the changes.

[0084] With the present disclosure, the implementation process becomes smoother, the risk of errors is reduced, and overall efficiency is improved.

Claims

1. A system (110) for optimizing the impact of service outages in a network, the system (110) comprising: one or more processors (202); a memory (204) operably coupled to the processor (202), the memory (204) storing instructions which, when executed by the processor (202), receive at least one data packet containing one or more parameters from one or more computing devices (106) associated with one or more active users (102) within at least one cell (104); analyze the at least one data packet to identify one or more patterns in a time series and correlate them with the one or more parameters; predict a lean time of the one or more computing devices (106) based on a time series prediction of the at least one data packet; cause the processor (202) to change the configuration of the one or more computing devices (106) at the lean time to minimize the one or more active users (102) and optimize the impact of service outages in the network. A system (110).

2. The system (110) according to claim 1, wherein the one or more parameters include at least one of network key performance indicators (KPIs), alarms, and faults.

3. The system (110) according to claim 1, wherein the one or more patterns include at least one of trends, seasonality, white noise, and random noise.

4. The one or more processors (202) are configured to receive the at least one data packet from the one or more computing devices (106) based on a predetermined time, the predetermined time being related to the aggregation of at least one data packet within a bin every hour of each day. The system (110) according to claim 1.

5. The one or more processors (202) are configured to check whether the state of the one or more computing devices (106) corresponds to the lean time. ​ ​ configured to schedule activity time for the one or more computing devices (106) based on the lean time, the activity time being related to the execution of a network service impact configuration change operation in the one or more computing devices (106), the system (110) of claim 1. [

6. ] The system (110) of claim 5, wherein the network service impact configuration change operation includes at least one of a firmware upgrade, a reconfiguration of network devices, a migration of network services, and an optimization of network capacity. [

7. ] The one or more processors (202) configured to sort the at least one data packet, exclude the one or more outliers, and obtain a time series prediction based on predicting the lean time of the one or more computing devices (106), the system (110) of claim 1. [

8. ] The lean time of the one or more computing devices (106) is predicted based on the one or more categories of one or more active users (102) of the one or more computing devices (106), the one or more categories including at least one of a low load category, a medium load category, and a high load category, the system (110) of claim 1. [

9. ] The time series relates to a set of one or more observed values, each observed value being associated with a specific time index, the system (110) of claim 1. [

10. ] A method for optimizing the impact of service outages in a network, the method comprising: receiving, by one or more processors (202), at least one data packet including one or more parameters from one or more computing devices (106) associated with one or more active users (102) within at least one cell (104); analyzing, by the one or more processors (202), the at least one data packet to identify one or more patterns in a time series and correlate them with the one or more parameters; Predicting, by the one or more processors (202), a lean time of the one or more computing devices (106) based on time series prediction; Executing, by the one or more processors (202), a change in the configuration of the one or more computing devices (106) in the lean time by minimizing the one or more active users (102) and optimizing the impact of service interruption in the network. A method comprising: **Claim 11** The method according to claim 10, wherein the one or more parameters include at least one of network key performance indicators (KPIs), alarms, and faults. **Claim 12** The method according to claim 10, wherein the one or more patterns include at least one of trends, seasonality, white noise, and random noise. **Claim 13** Receiving, by the one or more processors (202), the at least one data packet from the one or more computing devices (106) based on a predetermined time, wherein the predetermined time is related to the aggregation of at least one data packet within a bin every hour of each day. The method according to claim 10, comprising the step. **Claim 14** Confirming, by the one or more processors (202), whether the state of the one or more computing devices (106) corresponds to the lean time; Scheduling, by the one or more processors (202), an activity time for the one or more computing devices (106) based on the lean time, wherein the activity time is related to the execution of a network service impact configuration change operation in the one or more computing devices (106). The method according to claim 10, comprising the step. **Claim 15** The method according to claim 14, wherein the network service impact configuration change operation includes at least one of firmware upgrade, network device reconfiguration, network service migration, and network capacity optimization. **Claim 16** The method according to claim 10, comprising the step of sorting the at least one data packet by the one or more processors (202), eliminating the one or more outliers, and obtaining a time series prediction based on predicting a lean time of the one or more computing devices (106).

17. The lean time of the one or more computing devices (106) is predicted based on one or more categories of one or more active users (102) of the one or more computing devices (106), and the one or more categories include at least one of a low load category, a medium load category, and a high load category. The method according to claim 10.

18. A user equipment (UE) (104), one or more processors; a memory (204) operably coupled to the processor (202), wherein instructions are stored in the memory (204), and when the instructions are executed by the processor (202), identifying one or more temporary service interruptions during a configuration process; transmitting at least one data packet including one or more parameters to a system (110), the one or more parameters including at least one of a network key performance indicator (KPI), an alarm, and a fault; the step; receiving and executing one or more instructions from a system (110) during a lean time predicted based on a time series prediction to optimize the impact of service interruption in a network. A user equipment (UE) (104) that causes the processor (202) to perform.

19. A non-transitory computer-readable medium including machine-executable instructions executable by one or more processors, the machine-executable instructions, when executed by the one or more processors, receiving at least one data packet including one or more parameters from one or more computing devices (106) associated with one or more active users (102) in a cell; analyzing the at least one data packet to identify one or more patterns in a time series and correlating with one or more parameters. Based on the time series prediction of the at least one data packet, predict the lean time of the one or more computing devices (106), A non-transitory computer-readable medium that changes the configuration of the one or more computing devices (106) at the lean time to minimize one or more active users (102) and optimize the impact of service interruption in the network.

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