Method for distributing networks in high-density environments

Abstract

A network distribution method is disclosed, which is performed by a network system according to one embodiment of the present disclosure. The method comprises: monitoring network usage patterns of a plurality of devices connected to an electronic device included in the network system; determining, based on the monitoring, a device on which traffic is concentrated by time period; dynamically redistributing network resources to the plurality of devices based on information on the device on which traffic is concentrated by time period; and adjusting the channels of the AP (Access Point) devices based on the channel allocation status of two or more AP devices among the plurality of devices. The electronic device included in the network system may be a Power over Ethernet (PoE) hub device that supplies Direct Current (DC) power and data together to the plurality of devices.

Description

Technology Field

[0001] The present invention relates to a method for distributing a network, and more specifically, to a technology for effectively distributing a network in a high-density network environment. Background Technology

[0003] Since the COVID-19 pandemic, the need for non-face-to-face education and public services has increased, leading to a rise in digital learning and public services; consequently, stable and high-speed network infrastructure has become an essential requirement. In schools and public institutions, network traffic has surged as a large number of students use smart devices simultaneously, resulting in issues such as degraded line speeds and connection instability. Existing network infrastructure struggles to efficiently process data in high-density user environments, and frequent network latency and disconnections occur, increasing the need for technologies that can effectively distribute networks in such high-density settings.

[0004] Korean Patent No. 10-1947354 discloses a system for allocating network bandwidth among competing applications. The problem to be solved

[0006] Network distribution technology is needed to provide stable network services even when multiple users connect simultaneously.

[0007] The present disclosure aims to propose a method for effectively redistributing network resources through real-time network usage pattern analysis of a plurality of devices connected to a Power over Ethernet (PoE) hub device.

[0008] Meanwhile, the technical problem that the present disclosure aims to solve is not limited to the technical problem mentioned above, and various technical problems may be included within the scope obvious to a person skilled in the art from the contents described below. means of solving the problem

[0010] According to one embodiment of the present disclosure for realizing the aforementioned objectives, a network distribution method performed by a network system is disclosed. The method comprises: an operation of monitoring network usage patterns of a plurality of devices connected to an electronic device included in the network system; an operation of determining a device where traffic is concentrated by time period based on the monitoring; an operation of dynamically redistributing network resources to the plurality of devices based on information on the device where traffic is concentrated by time period; and an operation of adjusting the channels of the AP (Access Point) devices based on the channel allocation status of two or more AP (Access Point) devices among the plurality of devices. The electronic device included in the network system may be a Power over Ethernet (PoE) hub device that supplies Direct Current (DC) power and data together to the plurality of devices.

[0011] According to one embodiment, an electronic device included in the network system may be configured to perform an operation of distributing power according to the power consumption patterns of the plurality of devices over time.

[0012] According to one embodiment, the operation of distributing power according to the time-varying power consumption pattern of the plurality of devices may include: the operation of distributing power based on the time-varying power demand of the plurality of devices; and, when the sum of the power demands of the plurality of devices is greater than or equal to a threshold, the operation of receiving power from an additional power supply device and distributing power based on the power demand.

[0014] A computer program stored on a computer-readable storage medium is disclosed in accordance with one embodiment of the present disclosure for realizing the aforementioned objectives. When the computer program is executed on one or more processors, the one or more processors are configured to perform the following operations for distributing a network, wherein the operations include: monitoring the network usage patterns of a plurality of devices connected to an electronic device included in the network system; determining, based on the monitoring, a device where traffic is concentrated by time period; dynamically redistributing network resources to the plurality of devices based on the information on the device where traffic is concentrated by time period; and controlling the channels of the AP devices based on the channel allocation status of two or more AP (Access Point) devices among the plurality of devices, wherein the electronic device included in the network system may be a Power over Ethernet (PoE) hub device that supplies both Direct Current (DC) power and data to the plurality of devices.

[0016] A network system according to one embodiment of the present disclosure is disclosed for realizing the aforementioned objectives. The system comprises: a plurality of devices including two or more Access Point (AP) devices; a Power over Ethernet (PoE) hub device connected to the plurality of devices and supplying Direct Current (DC) power and data together to the plurality of devices; and a network control device, wherein the network control device comprises at least one processor and a memory, and the at least one processor may be configured to monitor the network usage patterns of the plurality of devices connected to the hub device, determine, based on the monitoring, a device where traffic is concentrated by time period, dynamically redistribute network resources to the plurality of devices based on the information on the device where traffic is concentrated by time period, and adjust the channels of the AP devices based on the channel allocation status of two or more Access Point (AP) devices among the plurality of devices. Effects of the invention

[0018] According to the present disclosure, optimal network performance can be maintained by monitoring network usage patterns in real time and dynamically redistributing network resources to traffic concentrators.

[0019] According to the present disclosure, power can be effectively distributed by monitoring the power consumption pattern of a device connected to a Power over Ethernet (PoE) hub device and adjusting the power in real time.

[0020] According to the present disclosure, a more stable network environment can be provided to user terminals connected to AP devices by minimizing the overlap and interference of channels of AP (Access Point) devices based on the channel allocation status of AP devices.

[0021] Meanwhile, the effects of the present disclosure are not limited to those mentioned above, and various effects may be included within the scope obvious to a person skilled in the art from the contents described below. Brief explanation of the drawing

[0023] FIG. 1 is a block diagram of an electronic device for controlling a network according to one embodiment of the present disclosure. FIG. 2 is a block diagram of a plurality of modules to explain the operation of a network control device effectively distributing a network in a high-density environment network system according to one embodiment of the present disclosure. FIG. 3 is a flowchart illustrating a method for distributing a network according to one embodiment of the present disclosure. FIG. 4 is a flowchart illustrating a method for controlling the channels of Access Point (AP) devices based on the channel allocation status of the AP devices according to one embodiment of the present disclosure. FIG. 5 is a flowchart illustrating a power distribution method for a Power over Ethernet (PoE) hub device according to one embodiment of the present disclosure. FIG. 6 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented. Specific details for implementing the invention

[0024] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to provide an understanding of the present disclosure. However, it is evident that these embodiments can be practiced without such specific descriptions.

[0025] As used herein, terms such as “component,” “module,” “system,” etc. refer to computer-related entities, hardware, firmware, software, combinations of software and hardware, or executions of software. For example, a component may be, but is not limited to, a procedure executed on a processor, a processor, an object, an execution thread, a program, and / or a computer. For example, both an application executed on a computing device and the computing device itself may be a component. One or more components may reside within a processor and / or an execution thread. A component may be localized within a single computer. A component may be distributed among two or more computers. Additionally, these components may be executed from various computer-readable media having various data structures stored therein. Components may communicate through local and / or remote processes, for example, according to signals having one or more data packets (e.g., data from a component interacting with another component in a local system or distributed system, and / or data transmitted through signals to other systems and networks such as the Internet).

[0026] Furthermore, the term "or" is intended to mean an implicit "or" rather than an exclusive "or." That is, unless otherwise specified or evident from the context, "X uses A or B" is intended to mean one of the natural implicit substitutions. In other words, if X uses A; if X uses B; or if X uses both A and B, "X uses A or B" may apply to any of these cases. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the enumerated related items.

[0027] Additionally, the terms “comprising” and / or “comprising” should be understood to mean that such features and / or components are present. However, the terms “comprising” and / or “comprising” should be understood not to exclude the presence or addition of one or more other features, components and / or groups thereof. Furthermore, unless otherwise specified or clearly evident from the context to indicate a singular form, the singular in this specification and claims should generally be interpreted to mean “one or more.”

[0028] And, the term "at least one of A or B" should be interpreted to mean "a case including only A," "a case including only B," or "a case combined with the composition of A and B."

[0029] Those skilled in the art should recognize that the various exemplary logical blocks, configurations, modules, circuits, means, logics, and algorithmic steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly exemplify the interchangeability of hardware and software, various exemplary components, blocks, configurations, means, logics, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented in hardware or software depends on the specific application and design constraints imposed on the overall system. Skilled technicians may implement the described functionality in various ways for each specific application. However, such decisions regarding implementation should not be construed as going beyond the scope of this disclosure.

[0030] The description of the presented embodiments is provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

[0031] In the present disclosure, network functions, artificial neural networks, and neural networks may be used interchangeably.

[0033] FIG. 1 is a block diagram of an electronic device (100) for controlling a network according to one embodiment of the present disclosure.

[0034] The configuration of the network control device (100) illustrated in FIG. 1 is merely a simplified example, and depending on the embodiment, the network control device (100) may be a computing device of various forms. In one embodiment of the present disclosure, the device (100) may include other configurations for performing the computing environment of the device (100), and only some of the disclosed configurations may constitute the device (100).

[0035] The device (100) may include a processor (110), memory (130), and a network unit (150). The processor (110) may be composed of one or more cores and may include a processor for data analysis and deep learning, such as a central processing unit (CPU) of a network control device, a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU). The processor (110) may read a computer program stored in memory (130) and perform data processing for machine learning according to one embodiment of the present disclosure. According to one embodiment of the present disclosure, the processor (110) may perform calculations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) can process the learning of a network function. For example, the CPU and GPGPU can together process the learning of a network function and data classification using the network function. In addition, in one embodiment of the present disclosure, processors of a plurality of computing devices can be used together to process the learning of a network function and data classification using the network function. In addition, a computer program executed in a network control device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0036] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0037] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk. The network control device (100) may operate in connection with web storage that performs the storage function of the memory (130) on the internet. The description of the memory described above is merely an example and the present disclosure is not limited thereto.

[0038] A network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0039] In addition, the network unit (150) presented in this specification may use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA), and other systems.

[0040] In the present disclosure, the network unit (150) can be configured regardless of the communication mode, such as wired and wireless, and can be configured as various communication networks such as a Local Area Network (LAN), a Personal Area Network (PAN), and a Wide Area Network (WAN). In addition, the network may be a known World Wide Web (WWW) and may utilize wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth.

[0041] The technologies described in this specification can be used not only in the networks mentioned above but also in other networks.

[0043] FIG. 2 is a block diagram of a plurality of modules to explain the operation of a network control device effectively distributing a network in a high-density environment network system according to one embodiment of the present disclosure.

[0044] According to one embodiment, the network control device (100) may be composed of a network monitoring module (210) for monitoring network usage patterns of a plurality of devices (260, 270) included in a network system, a network redistribution module (220) for determining a device where traffic is concentrated by time period according to monitoring and dynamically redistributing network resources to a plurality of devices (260, 270), an AP device channel control module (230) for controlling the channels of AP devices (262, 263, 265) based on the channel allocation status of two or more AP (Access Point) devices (261, 263, 265) among the plurality of devices, and a visualization and display module (240) for visualizing and displaying information regarding the network resource redistribution status and the channel allocation status of AP devices controlled in real time.

[0045] However, for the convenience of explanation, the various modules within the network control device (100) are described only in the form of modules, and depending on the embodiment, the various modules within the network control device (100) may be configured as separate devices from the network control device (100), and the multiple modules for distributing the network described herein are not limited to the modules shown in FIG. 2. For example, as described above with reference to FIG. 1, the network control device (100) may also include a control module that performs the function of a processor (110) for control.

[0046] According to one embodiment, a plurality of devices (260, 270) included in a network system may receive both power and data from a Power over Ethernet (PoE) hub device (250). The plurality of devices may include, in addition to AP (Access Point) devices (260) such as AP devices (261, 263, 265) that distribute the network to other terminals (280), network devices (270) such as an IP camera (271), a VoIP phone (273), and an IoT device (275). By supplying power as well as data to these plurality of devices, the PoE hub device (250) eliminates the need to install the plurality of devices near a power outlet. Since separate power cables or outlets are not required and the number of cables is reduced, installation time is shortened and complexity can be reduced when connecting multiple devices. Additionally, the PoE hub device (250) can provide the flexibility to easily add and remove devices that require both power and network.

[0047] According to one embodiment, the PoE hub device (250) can distribute power according to the time-based power consumption patterns of a plurality of devices (260, 270). For example, power is distributed according to the time-based power consumption patterns of a plurality of devices (260, 270), and when the sum of the power demands of the plurality of devices (260, 270) is greater than or equal to a threshold, power can be supplied from an additional power supply device (255) and distributed based on the power demand. The power distribution operation of the PoE hub device (250) will be described in detail later with reference to FIG. 5.

[0048] According to one embodiment, the network monitoring module (210) can monitor the network usage patterns of a plurality of devices (260, 270) included in the network system. The network redistribution module (220) can determine the device where traffic is concentrated by time period based on the network usage patterns of the plurality of devices (260, 270), and dynamically redistribute network resources to the plurality of devices (260, 270) based thereon. Specifically, the network redistribution module (220) can determine the device where traffic is concentrated by time period based on <Equation 1>.

[0049] <Mathematical Formula 1>

[0050]

[0051] Here, time zone It is the device that uses the most traffic, and is a set of N multiple devices connected to a PoE hub device (250), and is a device that is one of N multiple devices time zone This is the network bandwidth used. For example, referring to FIG. 2, among a plurality of devices (261, 263, 265, 271, 273, 275) connected to a PoE hub device (250), the time zone If the network bandwidth used by AP device 2 (263) is the largest, the network redistribution module (220) [uses] AP device 2 (263) Decide as.

[0052] According to one embodiment, the network redistribution module (220) can dynamically redistribute network resources to a plurality of devices (261, 263, 265, 271, 273, 275) based on time-based traffic concentration device information. Specifically, network redistribution can be performed based on <Equation 2>.

[0053] <Mathematical Formula 2>

[0054]

[0055] Here, is a device Time zone to It is a network resource to be allocated to, and is a weight that considers traffic usage from past time periods, and time zone and time zone It is a weighting function based on the temporal distance between, is a device determined based on at least one of usage, importance, and user settings. It is the priority of, and time zone It may be the total network resources available. According to one embodiment, a weight function It can be determined based on <Equation 3>.

[0056] <Mathematical Formula 3>

[0057]

[0058] According to one embodiment, the network redistribution module (220) can allocate a large amount of network resources to time-specific traffic concentrators that use a large amount of network bandwidth during a specific time period by redistributing the network based on <Equation 1>, <Equation 2>, and <Equation 3>. Specifically, with reference to <Equation 1>, the device time zone Network bandwidth used for As it gets larger, the time zone device network resources allocated to Network resources can be distributed to increase the size.

[0059] According to one embodiment, the AP device channel control module (230) can control the channels of the AP devices based on the channel allocation status of two or more AP devices (261, 263, 265) among a plurality of devices (261, 263, 265, 271, 273, 275). For example, the AP device channel control module (230) can determine the frequency band of each AP device to be either a 2.4 GHz band or a 5 GHz band based on the signal strength of a terminal (280) connected to the AP devices, and can control the channels of the AP devices so that they do not overlap or are adjacent. Specifically, when the frequency band is 2.4 GHz, the AP device channel control module (230) can allocate a channel that does not overlap with the previously allocated channels among channel 1, channel 6, and channel 11, and when the frequency band is 5 GHz, it can allocate a channel by considering the center frequency of the previously allocated channel.

[0060] For example, multiple terminals (280) are connected to AP devices (261, 263, 265), and if the signal strength of a terminal connected to AP device 1 (261) is greater than that of AP device 2 (263) and AP device 3 (265), a frequency band of 5 GHz may be assigned to AP device 1 (261), and a frequency band of 2.4 GHz may be assigned to AP device 2 (263) and AP device 3 (265). "Signal strength of connected terminal" is a measure indicating the degree of attenuation of a signal transmitted from the AP device until it reaches the terminal receiver, and can be measured in units of dBm (decibels in milliwatts).

[0061] For example, if another AP device 4 (not shown) not shown in FIG. 2 is already using channel 36 of the 5 GHz frequency band, the AP device channel control module (230) can assign AP device 1 (261) to channel 48, which has a center frequency far from channel 36, by taking into account the center frequency of channel 36 that has already been assigned among the channels within the 5 GHz frequency band.

[0062] As another example, if AP device 2 (263) allocated to the 2.4 GHz frequency band is using channel 1, the AP device channel control module (230) can allocate channel 11, which does not overlap with channel 1 among channels 1, 6, and 11, to another AP device 3 (265) allocated to the 2.4 GHz frequency band.

[0063] According to one embodiment, the operation of the network redistribution module (220) and the AP device channel control module (230) can be performed in real time, and information regarding the real-time network status and real-time channel allocation status can be displayed through the visualization and display module (240). According to one embodiment, the visualization and display module (240) can serve as a channel for receiving input from a user and outputting results. User input may include tactile input such as touch, slide, or drag, or voice input. For example, the user can directly input network redistribution or AP device channel allocation to the network control device (100).

[0064] According to one embodiment, information input through the visualization and display module (240) can be exchanged between the network control device (100), the PoE hub device (250), the additional power supply device (255), and a plurality of network devices (260, 270). For example, the network control device (100) can output results from the operation of other modules (210, 220, 230) through the visualization and display module (240) during the process of redistributing the network and allocating the AP device channel.

[0065] Below, based on the details described above, we will briefly examine the operation flow of the present invention with reference to FIGS. 3 to 5.

[0067] FIG. 3 is a flowchart illustrating a method for distributing a network according to one embodiment of the present disclosure.

[0068] The network distribution method illustrated in FIG. 3 can be performed by the network control device (100) described above. Therefore, even if the content is omitted below, the content described above regarding the network control device (100) can be applied in the same way to the description of the network distribution method described below.

[0069] Referring to FIG. 3, the network distribution method may include the steps of: monitoring the network usage patterns of a plurality of devices (260, 270) connected to a PoE device (250) (310); determining a device where traffic is concentrated by time period based on the monitoring (330); dynamically redistributing network resources to a plurality of devices based on information on the device where traffic is concentrated by time period (350); and adjusting the channels of the AP devices based on the channel allocation status of two or more AP devices among the plurality of devices (370).

[0070] Step 310 is a step of monitoring the network usage patterns of a plurality of devices (260, 270) connected to the PoE device (250). According to one embodiment, Step 310 may be performed by the network monitoring module (210) described above with reference to FIG. 2.

[0071] Step 330 is a step of determining a time-based traffic concentrator based on monitoring. According to one embodiment, Step 330 may be performed by the network redistribution module (220) described above with reference to FIG. 2. As described above with reference to FIG. 2, the time-based traffic concentrator may be determined based on <Equation 1>.

[0072] Step 350 is an operation of dynamically redistributing network resources to a plurality of devices based on time-based traffic concentration device information. According to one embodiment, Step 350 can be performed by the network redistribution module (220) described above with reference to FIG. 2. As described above with reference to FIG. 2, network redistribution for a plurality of devices (260, 270) can be performed based on <Equation 2> and <Equation 3>.

[0073] Step 370 is a step of adjusting the channels of AP devices, which can be performed by the AP device channel adjustment module (230) described above with reference to FIG. 2. A detailed operation for adjusting the channels of AP devices will be described later with reference to FIG. 4.

[0074] In the description above, steps 310 through 370 may be further divided into additional steps or combined into fewer steps according to embodiments of the present invention. Additionally, some steps may be omitted as necessary, and the order between steps may be changed.

[0076] FIG. 4 is a flowchart illustrating a method for controlling the channels of Access Point (AP) devices based on the channel allocation status of the AP devices according to one embodiment of the present disclosure.

[0077] After dynamically redistributing network resources in step 350, the process leads to step 370, which adjusts the channels of the AP devices. According to one embodiment, the step of adjusting the channels of the AP devices may include step 420, which determines the frequency band of each AP device based on the signal strength of terminals connected to the AP devices, and step 470, which adjusts the channels of the AP devices so that they do not overlap or become adjacent. According to one embodiment, steps 420 and 470 may be performed by the AP device channel adjustment module (230) described above with reference to FIG. 2.

[0078] In step 420, if the signal strength of the connected terminal is strong, the 5GHz band among the 2.4GHz band and the 5GHz band may be allocated to the AP device. As described above with reference to FIG. 2, the 5GHz frequency band may be allocated to the AP device with a high signal strength of the connected terminal (e.g., AP device 1 (261) described above with reference to FIG. 2), and the 2.4GHz frequency band may be allocated to the AP device with a relatively low signal strength of the connected terminal (e.g., AP device 2 (263), AP device 3 (265) described above with reference to FIG. 2).

[0079] In step 470, the operation of adjusting the channels of the AP devices so that they do not overlap or are adjacent may be performed differently for each frequency band. For example, as described above with reference to FIG. 2, for an AP device with a frequency band of 2.4 GHz (e.g., AP device 2 (263), AP device 3 (265) described with reference to FIG. 2), a channel that does not overlap with the previously assigned channel among channels 1, 6, and 11 is assigned, and for an AP device with a frequency band of 5 GHz (e.g., AP device 1 (261) described with reference to FIG. 2), a channel may be assigned by taking into account the center frequency of the previously assigned channel.

[0080] In the description above, steps 420 and 470 may be further subdivided into additional steps or combined into fewer steps, depending on the embodiment of the present invention. Additionally, some steps may be omitted as necessary, and the order between steps may be changed.

[0082] FIG. 5 is a flowchart illustrating a power distribution method for a Power over Ethernet (PoE) hub device according to one embodiment of the present disclosure.

[0083] The power distribution method of the PoE hub device illustrated in FIG. 5 can be performed by the PoE hub device (250) described above. Therefore, even if the details are omitted below, the details described above regarding the PoE hub device (250) can be applied equally to the description of the power distribution method described below.

[0084] Referring to FIG. 5, a power distribution method for a PoE hub device may include a step 510 of distributing power based on the time-based power demand of a plurality of devices connected to the PoE device, a step 530 of determining whether the sum of the power demands of the plurality of devices is greater than or equal to a threshold, and a step 550 of distributing power based on power demand by receiving power from an additional power supply device when the sum of the power demands of the plurality of devices is greater than or equal to a threshold.

[0085] According to one embodiment, the power distribution operation of the PoE hub device can be performed by the PoE hub device (250) described above with reference to FIG. 2. For example, in step 510, the PoE hub device (250) can distribute power based on the time-based power demand of a plurality of connected devices (260, 270). In another example, if the sum of the power demands of the plurality of devices (260, 270) in step 530 is greater than or equal to a threshold, the PoE hub device (250) can receive power from an additional power supply device (255) in step 550 and distribute power according to the power demand of the plurality of devices (260, 270).

[0086] In the description above, steps 510 to 550 may be further divided into additional steps or combined into fewer steps according to embodiments of the present invention. Additionally, some steps may be omitted as necessary, and the order of the steps may be changed.

[0088] According to one embodiment of the present disclosure, a computer-readable medium storing a data structure is disclosed.

[0089] A data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. A data structure can refer to the organization of data for solving specific problems (e.g., data retrieval, data storage, data modification in the shortest possible time). A data structure may also be defined by physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements may include connections between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., a permanent storage device). Specifically, a data structure may include sets of data, relationships between data, and functions or instructions applicable to the data. Through an effectively designed data structure, a network control device can perform operations while minimizing the use of its resources. Specifically, through an effectively designed data structure, a network control device can improve the efficiency of operations, reading, insertion, deletion, comparison, exchange, and retrieval.

[0090] Data structures can be classified into linear and non-linear data structures based on their form. A linear data structure is one where only one piece of data is connected to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a set of data that maintains an internal order. Lists can include linked lists. A linked list is a data structure where data is connected in a line, with each piece of data possessing a pointer. In a linked list, the pointer can contain information regarding the connection to the next or previous data. Depending on its form, a linked list can be represented as a singly linked list, a doubly linked list, or a circular linked list. A stack is a data arrangement structure that allows for restricted access to data. A stack can be a linear data structure where data can be processed (e.g., insertion or deletion) only at one end. Data stored in a stack can be a Last-In, First-Out (LIFO) data structure, meaning that the later an item is entered, the sooner it is retrieved. A queue is a data sequence structure that allows for limited access to data; unlike a stack, it can be a FIFO (First in First Out) data structure where data stored later is retrieved later. A deque is a data structure that can process data at both ends.

[0091] Non-linear data structures can be structures where multiple data are connected after a single piece of data. Non-linear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge may include a line connecting two different vertices. Graph data structures may include tree data structures. A tree data structure may be a data structure where there is only one path connecting two different vertices among the multiple vertices included in the tree. In other words, it may be a data structure that does not form a loop in a graph data structure.

[0092] Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, the term neural network will be used consistently. A data structure may include a neural network. Furthermore, a data structure including a neural network may be stored on a computer-readable medium. A data structure including a neural network may also include data preprocessed for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. A data structure including a neural network may include any of the components disclosed above. That is, a data structure including a neural network may be configured to include all or any combination thereof, such as data preprocessed for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for learning the neural network. In addition to the configurations described above, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated during the computational process of the neural network, and is not limited to the foregoing. A computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units that may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0093] A data structure may include data input to a neural network. A data structure including data input to a neural network may be stored on a computer-readable medium. Data input to a neural network may include training data input during the neural network learning process and / or input data input to a neural network after training is complete. Data input to a neural network may include pre-processed data and / or data subject to pre-processing. Pre-processing may include a data processing process for inputting data into a neural network. Accordingly, a data structure may include data subject to pre-processing and data generated by pre-processing. The aforementioned data structure is merely an example, and the present disclosure is not limited thereto.

[0094] The data structure may include weights of the neural network. (In this specification, weights and parameters may be used interchangeably.) The data structure including the weights of the neural network may be stored on a computer-readable medium. The neural network may include multiple weights. The weights may be variable and may be varied by a user or an algorithm to enable the neural network to perform a desired function. For example, if one or more input nodes are interconnected to a single output node by respective links, the output node may determine the data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set on the links corresponding to each input node. The aforementioned data structure is merely an example and the present disclosure is not limited thereto.

[0095] As an example rather than a limitation, weights may include weights that vary during the neural network learning process and / or weights for which neural network learning is completed. Weights that vary during the neural network learning process may include weights at the start of the learning cycle and / or weights that vary during the learning cycle. Weights for which neural network learning is completed may include weights for which the learning cycle is completed. Accordingly, a data structure containing the weights of a neural network may include a data structure containing weights that vary during the neural network learning process and / or weights for which neural network learning is completed. Therefore, the weights and / or combinations of each weight described above are included in the data structure containing the weights of a neural network. The aforementioned data structure is merely an example and the present disclosure is not limited thereto.

[0096] Data structures containing the weights of a neural network may be stored on a computer-readable storage medium (e.g., memory, hard disk) after undergoing a serialization process. Serialization may be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed for use. A computing device may serialize the data structure to transmit and receive data over a network. A serialized data structure containing the weights of a neural network may be reconstructed on the same or different computing devices through deserialization. Data structures containing the weights of a neural network are not limited to serialization. Furthermore, data structures containing the weights of a neural network may include data structures designed to increase computational efficiency while minimizing the use of computing device resources (e.g., B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree in non-linear data structures). The foregoing is merely an example and the present disclosure is not limited thereto.

[0097] The data structure may include hyperparameters of the neural network. The data structure including the neural network hyperparameters may be stored on a computer-readable medium. The hyperparameters may be variables that are varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle iterations, weight initialization (e.g., setting the range of weight values ​​subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layers). The aforementioned data structure is merely an example, and the present disclosure is not limited thereto.

[0099] FIG. 6 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0100] Although the present disclosure has been described as generally being implementable by a computing device, those skilled in the art will understand that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers, and / or as a combination of hardware and software.

[0101] Generally, a program module includes routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, those skilled in the art will be well aware that the method of the present disclosure may be implemented in other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which may be connected to and operated with one or more associated devices).

[0102] The embodiments described in this disclosure may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0103] Computers typically include various computer-readable media. Any medium accessible by a computer may be a computer-readable medium, and such computer-readable media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media. By example, but not limiting, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be accessed by a computer and used to store desired information.

[0104] Computer-readable transmission media typically include all information transmission media that implement computer-readable instructions, data structures, program modules, or other data, etc., on a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal in which one or more of the characteristics of the signal are set or modified to encode information within the signal. By example, not limiting, computer-readable transmission media include wired media, such as wired networks or direct-wired connections, and wireless media, such as acoustic, RF, infrared, and other wireless media. Any combination of the media described above is also considered to be within the scope of computer-readable transmission media.

[0105] An exemplary environment (600) for implementing various aspects of the present disclosure, including a computer (602), is shown, wherein the computer (602) includes a processing unit (604), system memory (606), and a system bus (608). The system bus (608) connects system components, including system memory (606) (but not limited thereto), to the processing unit (604). The processing unit (604) may be any processor among various commercial processors. Dual processor and other multiprocessor architectures may also be used as the processing unit (604).

[0106] The system bus (608) may be any of several types of bus structures that can be additionally interconnected to a local bus using any of the memory bus, peripheral bus, and various commercial bus architectures. System memory (606) includes read-only memory (ROM) (610) and random access memory (RAM) (612). The basic input / output system (BIOS) is stored in non-volatile memory (610), such as ROM, EPROM, EEPROM, etc., and this BIOS includes basic routines that help transfer information between components within the computer (602) at times such as during startup. The RAM (612) may also include high-speed RAM, such as static RAM, for caching data.

[0107] The computer (602) also includes an internal hard disk drive (HDD) (614) (e.g., EIDE, SATA)—this internal hard disk drive (614) may also be configured for external use within a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) (616) (e.g., for reading from or writing to a removable diskette (618)), and an optical disk drive (620) (e.g., for reading from a CD-ROM disk (622) or reading from or writing to other high-capacity optical media such as a DVD). The hard disk drive (614), the magnetic disk drive (616), and the optical disk drive (620) may each be connected to the system bus (608) by a hard disk drive interface (624), a magnetic disk drive interface (626), and an optical drive interface (628). The interface (624) for implementing an external drive includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

[0108] These drives and associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of a computer (602), the drives and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will know that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., may also be used in exemplary operating environments and that any of these media may contain computer-executable instructions for performing the methods of the present disclosure.

[0109] A number of program modules, including an operating system (630), one or more application programs (632), other program modules (634), and program data (636), may be stored in the drive and RAM (612). All or part of the operating system, application, module, and / or data may also be cached in RAM (612). It will be well known that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0110] The user can input commands and information into the computer (602) through one or more wired / wireless input devices, such as a pointing device like a keyboard (638) and a mouse (640). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, etc. These and other input devices are often connected to the processing unit (604) via an input device interface (642) connected to the system bus (608), but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

[0111] A monitor (644) or other type of display device is also connected to the system bus (608) via an interface such as a video adapter (646). In addition to the monitor (644), the computer generally includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0112] The computer (602) may operate in a networked environment using a logical connection to one or more remote computers, such as remote computer(s) (648), via wired and / or wireless communication. The remote computer(s) (648) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (602), but for brevity, only the memory storage device (650) is illustrated. The illustrated logical connection includes a wired / wireless connection to a local area network (LAN) (652) and / or a larger network, e.g., a wide area network (WAN) (654). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global computer network, e.g., the Internet.

[0113] When used in a LAN networking environment, the computer (602) is connected to a local network (652) via a wired and / or wireless communication network interface or adapter (656). The adapter (656) may facilitate wired or wireless communication to the LAN (652), and the LAN (652) may also include a wireless access point installed therein to communicate with the wireless adapter (656). When used in a WAN networking environment, the computer (602) may include a modem (658), be connected to a communication computing device on the WAN (654), or have other means to establish communication over the WAN (654), such as through the Internet. The modem (658), which may be internal or external and wired or wireless, is connected to the system bus (608) via a serial port interface (642). In a networked environment, program modules or parts thereof described for the computer (602) may be stored in a remote memory / storage device (650). You will be well aware that the illustrated network connection is exemplary and that other means of establishing communication links between computers can be used.

[0114] The computer (602) operates to communicate with any wireless device or object that is deployed and operated via wireless communication, such as a printer, scanner, desktop and / or portable computer, PDA (portable data assistant), communication satellite, any equipment or place associated with a wireless detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or simply ad hoc communication between at least two devices.

[0115] Wi-Fi (Wireless Fidelity) enables connectivity to the Internet and other sources without wires. Wi-Fi is a wireless technology, similar to a cell phone, that allows devices, such as computers, to transmit and receive data indoors and outdoors—that is, anywhere within the coverage area of ​​a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band).

[0116] Those skilled in the art of the present disclosure will understand that information and signals may be represented using any various different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0117] Those skilled in the art will understand that the various exemplary logic blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented by electronic hardware, various forms of programs or design code (referred to herein as software for convenience), or a combination of all such. To clearly illustrate this interoperability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in relation to their functions. Whether such functions are implemented as hardware or software depends on the design constraints imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementation decisions should not be interpreted as being outside the scope of this disclosure.

[0118] The various embodiments presented herein may be implemented as methods, devices, or articles manufactured using standard programming and / or engineering techniques. The term "article manufactured" includes a computer program, a carrier, or a medium accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical discs (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, the various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0119] It should be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but do not imply being limited to the specific order or hierarchy presented.

[0120] Description of the presented embodiments is provided so that a person skilled in the art may use or practice the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments presented herein, but should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

Claims

Claim 1 A network distribution method performed by a network system, comprising: an operation of monitoring the network usage patterns of a plurality of devices connected to an electronic device included in the network system; an operation of determining a device where traffic is concentrated by time period based on the monitoring; an operation of dynamically redistributing network resources to the plurality of devices based on information on the device where traffic is concentrated by time period; and an operation of adjusting the channels of the AP (Access Point) devices based on the channel allocation status of two or more AP (Access Point) devices among the plurality of devices, wherein the electronic device included in the network system is a PoE (Power over Ethernet) hub device that supplies DC (Direct Current) power and data together to the plurality of devices. Claim 2 A network distribution method according to claim 1, wherein the electronic device included in the network system is configured to perform the operation of distributing power according to the power consumption pattern of the plurality of devices over time. Claim 3 A network distribution method according to paragraph 2, wherein the operation of distributing power according to the time-varying power consumption pattern of the plurality of devices comprises: the operation of distributing power based on the time-varying power demand of the plurality of devices; and the operation of distributing power based on the power demand by receiving power from an additional power supply device when the sum of the power demands of the plurality of devices is greater than or equal to a threshold. Claim 4 A computer program stored on a computer-readable storage medium, wherein, when the computer program is executed on one or more processors, the one or more processors perform the following operations for distributing a network, the operations include: an operation of monitoring the network usage patterns of a plurality of devices connected to an electronic device included in the network system; an operation of determining, based on the monitoring, a device where traffic is concentrated by time period; an operation of dynamically redistributing network resources to the plurality of devices based on information on the device where traffic is concentrated by time period; and an operation of adjusting the channels of the AP (Access Point) devices based on the channel allocation status of two or more AP (Access Point) devices among the plurality of devices, wherein the electronic device included in the network system is a PoE (Power over Ethernet) hub device that supplies DC (Direct Current) power and data together to the plurality of devices. Claim 5 A network system comprising: a plurality of devices including two or more Access Point (AP) devices; a Power over Ethernet (PoE) hub device connected to the plurality of devices and supplying Direct Current (DC) power and data to the plurality of devices; and a network control device, wherein the network control device comprises at least one processor and memory, and wherein the at least one processor is configured to monitor the network usage patterns of the plurality of devices connected to the hub device, determine, based on the monitoring, a device where traffic is concentrated by time period, dynamically redistribute network resources to the plurality of devices based on the information on the device where traffic is concentrated by time period, and adjust the channels of the AP devices based on the channel allocation status of two or more Access Point (AP) devices among the plurality of devices.

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