KR20230032383A - Collaboration system between edge servers based on computing resource prediction in intelligent video security environment - Google Patents

Abstract

Disclosed is a cooperation system between edge servers based on computing resource prediction in an intelligent video security environment. The cooperation system between edge servers based on computing resource prediction in an intelligent video security environment comprises one master edge server, a plurality of worker edge servers, and one or more terminals connected to each worker edge server. When connection control of a terminal and a worker edge server to perform autonomous cooperation with less usage of computing resources than a threshold is determined in the direction in which the dispersion of the computing resources of the overall worker edge servers is minimized to classify context for each element involved in connection with autonomous cooperation and reach a load balance based on classified context properties, the corresponding worker edge server and terminal determined from the master edge server are connected. The autonomous cooperation is performed by providing a load balance by minimizing a situation in which a load of the computing resources (CPU usage, RAM usage, and storage usage) of each worker edge server is created. The efficiency of an intelligent video security service is improved through real-time video analysis. The worker edge server includes a context extraction module, a context standardization module, a context sharing module, a terminal control module, and a connection result sharing module. The master edge server includes a context gathering module, a real-time context monitoring module, a connection control resource prediction module, an autonomous cooperation determination module, a terminal control result gathering module, and a terminal control result monitoring module.

Description

Collaboration system between edge servers based on computing resource prediction in intelligent video security environment {Collaboration system between edge servers based on computing resource prediction in intelligent video security environment}

The present invention relates to a collaboration system between edge servers based on computing resource prediction in an intelligent video security environment, and more particularly, consists of a master edge server, a plurality of worker edge servers equipped with analytical intelligence, and a plurality of terminals, For intelligent video security, the connection result context is periodically shared with the terminal, edge server, and context related to analytical intelligence, and autonomous collaboration between edge servers is performed based on computing resource prediction according to terminal connection control using the shared context. It's about the system.

The system classifies contexts for each element involved in autonomous collaborative linkage and uses computing resources above the threshold in a direction in which the distribution of computing resources of the entire worker edge server is minimized to achieve load balancing based on the classified context characteristics. When determining the connection control between the worker edge server and the terminal that will perform this small autonomous collaboration, the corresponding worker edge server and terminal determined from the master edge server are connected, and the computing resources of each worker edge server (CPU share, RAM share, storage usage) ) to provide load balancing by minimizing the situation in which the load of

Cloud computing processes transaction data in a client/server method in a centralized server, whereas edge computing is a computing method in contrast to cloud computing, which is not cloud computing or centralized. Computing takes place at the edge of the network. Edge computing does not process data in cloud computing or centralized central servers where the load of large amounts of data is concentrated, but as shown in FIG. server) is used to analyze data and operate the device.

1 is a conceptual diagram of intelligent edge computing.

Unlike cloud computing that processes data centrally, Mobile Edge Computing (MEC) technology collects, analyzes, and processes data individually from around mobile devices through distributed edge servers. Recently, with the development of LTE 4G/5G mobile communication technology, drone, and IoT technology, it is developing into an intelligent edge computing technology that uses big data analysis using artificial intelligence and machine learning (ML).

For example, CCTV intelligent image processing requires an overload of large-capacity image data stored in a central server and intelligent edge computing technology using multiple edge servers distributed by region to process large-capacity image data.

As related prior art 1, Patent Publication No. 10-2018-0119162 discloses a "platform for computing at the mobile edge".

A platform for computing at the mobile edge

As a platform for computing within a wireless network,

A Mobile Edge (MEC) comprising a hardware device located at or near the edge of a private management network and capable of providing data services and security mechanisms for running programs associated with a third party provider on the private management network. Compute) appliance;

An MEC controller that resides in a core network of a mobile network operator (MNO) and can control and command the MEC appliance; and

A plurality of components essential to the functioning of the MEC controller and extending throughout the MEC appliance, establishing a plurality of layers - each component to address a variety of attack vectors and potential exploits at each layer. (address) configured - includes the containing security module,

the platform provides computing resources for hosting at least one third party application within an environment that is at least one of a network perimeter of the private management network and near the network perimeter of the private management network;

The security module controls any action by any platform component in the environment that is at least one of the network perimeter of the private management network and the vicinity of the network perimeter of the private management network before access to the private management network is granted. configured to validate the action.

As related prior art 2, Patent Registration No. 10-2110592 has registered "Distributed processing system between cloud and edge in IoT computing environment".

Distributed processing system between cloud and edge in IoT computing environment

The cloud and edge in the IoT computing environment, including a plurality of edges connected to the object side to collect data and send control signals to the object side, and a cloud receiving and processing data from each edge and transmitting the processing result to the edge. As a distributed processing system between

The cloud includes a model creation unit for generating a model for processing the collected data, a model distribution rule for distributed processing between the cloud and the edge, and a model distribution unit for distributing the model to the edge according to the model distribution rule. and

The model distribution rule is determined according to at least one of processing performance and memory capacity of each edge, and the type and number of models distributed from the cloud to each edge are different according to the determined distribution rule.

As prior art 3 related to this, Patent Registration No. 10-2163280 "edge computing based network monitoring method, device and system" is registered.

An edge computing-based network monitoring method, apparatus, and system are provided in which a second entity is included in a network including a first entity and a second entity and a switching device provided between the first entity and the second entity. The second entity includes a transceiver for transmitting and receiving packets with the first entity, and the second entity includes a processor to perform edge computing, or the switching via an edge computing device including a processor. connected to the device,

The processor is configured to determine whether a security problem occurs in a network associated with the first entity and the second entity based on at least a portion of information included in at least one packet received by or transmitted from the second entity. It consists of

Determining whether a security problem occurs based on the packet information,

Determining that the number of connections in the section from the predetermined first source IP to the first destination IP is equal to or greater than a predetermined threshold, determining that the request for the predetermined first URL is equal to or greater than a predetermined threshold, and determining a predetermined BPS of the first server (Bit Per Second) is greater than or equal to a predetermined threshold value, determination that PPS (Packet Per Second) from a predetermined second source IP is greater than or equal to a predetermined threshold value, synchronization signal (SYN) from a predetermined third source IP Determining that a security problem has occurred in the network based on at least one of a determination that the number of packets is equal to or greater than a predetermined threshold, and a determination that the predetermined fourth source IP simultaneously attempts access to server IPs equal to or greater than the predetermined threshold. do.

With the development of network processing technology and smart terminals, the number of simultaneously connected terminals increases and various types of services appear, and intelligent video security technology using edge servers is attracting attention. In the case of video security technology based on an existing VMS (Video Management System), since a plurality of terminals share all information through a cloud such as a VMS, there is a problem in that service processing efficiency is reduced due to resource load and delay.

Intelligent video security technology using an edge server performs context recognition, real-time video analysis, and autonomous collaboration between edge servers in an edge server that is geographically close to the terminal to solve the problem of VMS-based video security technology.

However, for intelligent video security, master edge server and multiple worker edge servers exist in an intelligent video security environment where multiple worker edge servers and terminals exist by adaptive context sharing and autonomous collaboration system between master edge server and multiple worker edge servers. The efficiency of intelligent video security service based on real-time video analysis is improved by minimizing unnecessary context sharing of edge servers and performing autonomous collaboration to balance the load of computing resources (CPU occupancy, RAM occupancy, storage usage) of worker edge servers. Height has not yet provided a system.

Patent Publication No. 10-2018-0119162 (published on November 01, 2018), "Platform for Computing at the Mobile Edge", ACS (US), Inc. Patent Registration No. 10-2110592 (registration date: May 07, 2020), "Distributed processing method and system between cloud and edge in IoT computing environment", Korea Electronics Technology Institute Patent Registration No. 10-2163280 (Registration Date September 29, 2020), "Edge Computing-Based Network Monitoring Method, Apparatus and System", McData Co., Ltd.

The purpose of the present invention to solve the above problem is composed of a master edge server, a plurality of worker edge servers, and terminals, and periodically shares terminals, edge servers, and analytical intelligence-related contexts and linkage result contexts for intelligent video security. and performs autonomous collaboration between edge servers based on computing resource prediction based on terminal connection control using a shared context, classifies contexts by elements involved in autonomous collaboration type linkage, and load balances based on the classified context characteristics. When it is determined to control the connection between the worker edge server and the terminal to perform autonomous collaboration with less computing resource usage than the threshold in the direction in which the distribution of computing resources of the overall worker edge server is minimized to achieve balancing), the corresponding worker edge determined from the master edge server It connects the server and the terminal, minimizes the load of computing resources (CPU share, RAM share, storage usage) of each worker edge server, provides load balancing to perform autonomous collaboration, and real-time video Provides a collaboration system between edge servers based on computing resource prediction in an intelligent video security environment that improves the efficiency of intelligent video security services through analysis.

In order to achieve the object of the present invention, a collaboration system between edge servers based on computing resource prediction in an intelligent video security environment includes a terminal having a wireless communication unit; Equipped with analytical intelligence to receive and analyze video from the terminal, control terminal connection, extract and standardize the context of the edge server in real time, and periodically transfer the adaptively standardized context to the master edge server to the master edge server. A number of worker edge servers that transmit; And contexts shared from the plurality of worker edge servers are collected and stored, and reliability is calculated. It determines to perform control, and includes a master edge server that controls the corresponding Worker edge server to autonomously collaborate with new terminals through connection control.

In the intelligent video security environment of the present invention, the collaboration system between edge servers based on computing resource prediction is composed of a master edge server, a plurality of worker edge servers, and terminals. Periodically share the context as a result of linking with and use the shared context to perform autonomous collaboration between edge servers based on computing resource prediction according to terminal connection control, and classify the context for each element involved in the autonomous collaborative linkage and classify the context When determining the connection control between the worker edge server and the terminal to perform autonomous collaboration with less computing resource usage than the threshold in a direction in which the distribution of computing resources of the entire worker edge server is minimized to achieve load balancing based on characteristics, It connects the corresponding worker edge server and the terminal determined by the master edge server, and provides load balancing by minimizing the load of computing resources (CPU occupancy, RAM occupancy, storage usage) of each worker edge server. It is possible to perform autonomous collaboration and increase the efficiency of intelligent video security service through real-time video analysis.

Through adaptive context sharing for intelligent video security and an autonomous collaboration system between edge servers, a video security environment where a master edge server and multiple worker edge servers and terminals exist (smartphone video, CCTV video, car black box video, drone video) Intelligent video security service based on real-time video analysis by minimizing unnecessary context sharing between a master edge server and multiple worker edge servers in camera video) and performing autonomous collaboration by providing load balancing of computing resources of worker edge servers expected to increase the efficiency of

1 is a conceptual diagram of intelligent edge computing.
2 shows an autonomous collaboration scenario between edge servers according to the present invention.
3 shows the structure of a collaboration system between edge servers based on computing resource prediction in an intelligent video security environment according to the present invention.
4 shows context classification according to elements involved in a collaboration system between edge servers based on computing resource prediction.
5 shows an example of predicting usage of computing resources according to terminal connection control in a worker edge server.
6 is a flowchart for determining collaboration in an autonomous collaboration system between edge servers based on computing resource prediction.

Hereinafter, the configuration and operation of a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings. In the description of the present invention, if it is determined that a detailed description of a related known function or known configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. In addition, drawing numbers are assigned the same drawing numbers in different drawings when indicating the same configuration.

In the intelligent video security environment (smartphone video, CCTV video, car black box video, drone camera video) of the present invention, a collaboration system between edge servers based on computing resource prediction is a master edge server and a plurality of worker edge servers, and a plurality of It consists of terminals, and periodically shares terminals, edge servers, and contexts related to analytical intelligence and linkage result contexts for intelligent video security, and uses the shared context to autonomously collaborate between edge servers based on prediction of computing resources according to terminal connection control. to perform,

Contexts are classified for each element involved in the autonomous collaborative linkage, and load balancing is performed based on the classified context characteristics, so that the distribution of computing resources of the entire worker edge server is minimized. When the connection control between the worker edge server and the terminal to perform collaboration is determined, the corresponding worker edge server and terminal determined from the master edge server are connected, and the load of each worker edge server's computing resources (CPU share, RAM share, storage usage) It is possible to perform autonomous collaboration by providing load balancing by minimizing the occurrence of the situation, and to increase the efficiency of intelligent video security service through real-time video analysis.

Worker edge server computing resources include CPU occupancy, RAM occupancy, and storage usage.

In an intelligent video security environment, a collaboration system between edge servers based on computing resource prediction is a master edge server 100, a plurality of worker edge servers 200, a camera, a GPS receiver, and a wireless communication unit (WiFi, LTE 4G/5G communication unit, IoT It is composed of terminals 300 equipped with a communication unit).

For example, the terminals 300 include a smartphone having a camera, a GPS receiver, and a wireless communication unit (WiFi, LTE 4G/5G communication unit, IoT communication unit), a CCTV camera equipped with a camera, a GPS receiver, and a wireless communication unit, and a camera. and a vehicle equipped with a black box equipped with a GPS receiver and a wireless communication unit, and a drone equipped with a camera, a GPS receiver and a wireless communication unit are used.

The terminals 300 monitor targets that may cause security risks (traffic accidents, fires, etc.). In addition, the terminal 300 transmits the image of the object being monitored to the worker edge server 200 connected through a wireless network. An edge server cluster is configured using the master edge server 100 and a plurality of worker edge servers 200. The worker edge server 200 receives images from multiple terminals through a communication network and has analytical intelligence to recognize security risks. In addition, the worker edge server 200 has sufficient computing resources (CPU occupancy, RAM occupancy, storage usage) and can use analytical intelligence to analyze images received from terminals. Whenever an image is received from one or more terminals 300 connected to the worker edge server 200, the analytical intelligence analyzes the image and recognizes various security risks (traffic accident, fire, etc.) through this. In addition, the worker edge server 200 extracts and formalizes multiple contexts for autonomous collaboration between edge servers. The extracted and standardized context performs multi-type context sharing between the worker edge server 200 and the master edge server 100. The master edge server 100 performs autonomous collaboration between edge servers based on a shared context. The proposed system performs collaboration between edge servers for load balancing based on prediction of computing resources (CPU, RAM, storage). The master edge server 100 determines collaboration whenever a context related to a terminal connected to the worker edge server 200 changes. Collaboration between edge servers predicts changes in computing resources according to device connection control. Determines the terminal and edge server whose load balance is improved by using the predicted change in computing resources. In addition, to determine the terminal and edge server, the reliability calculated based on the predicted and actual usage of computing resources (CPU share, RAM share, storage usage) of the worker edge server is reflected.

2 shows an autonomous collaboration scenario between edge servers proposed in the present invention.

In an intelligent video security environment, the computing resource prediction-based collaboration system between edge servers has a structure in which one master edge server 100, a plurality of worker edge servers 200, and one or more terminals 300 are connected to each worker edge server. It consists of

A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment includes a terminal 300 having a wireless communication unit;

Equipped with analysis intelligence that receives and analyzes images from the terminal 300 equipped with the wireless communication unit, controls terminal connection, extracts and formalizes the context of the worker edge server in real time, and adapts to the master edge server 100 A plurality of worker edge servers 200 that periodically transmit a formalized context to the master edge server; and

Contexts shared from the plurality of worker edge servers 200 are collected and stored, reliability is calculated, and when the reliability is high, the worker edge server being monitored to achieve load balancing of the worker edge server ( 200) determines to control the connection of a new terminal to the worker edge server, which consumes less computing resources, and controls the determined corresponding worker edge server to autonomously collaborate with the terminal through connection control.

The terminal 300 is connected to the worker edge server 200 based on a wireless network. In addition, the terminal 300 takes a picture of an object and transmits it to the worker edge server 200 in order to recognize a security risk. Worker edge server 200 extracts and formalizes multiple contexts for autonomous collaboration between edge servers. The multi-species context includes the edge server's computing resources, terminals, and analytic intelligence. The worker edge server 200 shares the extracted and standardized context with the master edge server 100. In addition, the worker edge server 200 has analytical intelligence capable of recognizing security risks, and detects security risks using images received from the terminal 300. The master edge server 100 performs autonomous collaboration between the edge servers based on the multi-type context shared with the worker edge server 200. Autonomous collaboration between edge servers is performed when the number of terminals 300 connected to the worker edge server 200 is changed or the object recognized by the terminal 300 is changed. For example, when a terminal is newly connected to the worker edge server 200 and the computing resources of the corresponding worker edge server are insufficient to analyze the received image, the master edge server 100 determines collaboration between the worker edge servers. Collaboration decisions between worker edge servers are based on the multi-species context recognized by the master edge server 100 to improve the load balance between the worker edge servers 200 with relatively low (relaxed) usage of computing resources among other worker edge servers. The Worker Edge Server and the Worker Edge Server can process the video transmitted by the connected terminals and select the appropriate terminal for load balancing.

3 shows the structure of a collaboration system between edge servers based on computing resource prediction in an intelligent video security environment proposed in the present invention.

Worker edge server 200 includes analytical intelligence for analyzing images received from a plurality of terminals 300, and includes a context extraction module, a context formalization module, a context sharing module, a terminal control module, and a linkage result sharing module. do.

The context extraction module classifies and extracts contexts from terminals, worker edge servers, and analytical intelligence for autonomous collaboration between worker edge servers, and the classified and extracted contexts are transmitted to the context formalization module.

The context formalization module formalizes the classified and extracted contexts. The reason is to use it for autonomous collaboration between edge servers.

The context sharing module periodically transmits the standardized context to the master edge server 100.

The terminal control module performs autonomous collaboration determined by the master edge server 100.

The linkage result sharing module transmits the context created through autonomous collaboration between the worker edge servers 200 to the master edge server 100. The connection result is transmitted to the master edge server 100 and used for connection between edge servers.

The master edge server 100 includes a context aggregation module, a real-time context monitoring module, a connection control resource prediction module, an autonomous collaboration decision module, a terminal control result collection module, and a terminal control result monitoring module.

The context collection module of the master edge server 100 collects, stores, and manages multiple contexts received from a plurality of worker edge servers.

The real-time context monitoring module enables the context collected by the context collection module to be checked using a web page.

The connection control resource estimation module, based on the multi-type context information received from the context collection module, changes the number of terminals 300 connected to the worker edge server 200 or the object recognized by the terminal changes, the worker edge according to the terminal 300 The computing resource usage of the server 200 is predicted.

The autonomous collaboration decision module uses the computing resource prediction value received from the connection control resource prediction module to determine the worker edge server 200 and the terminal 300 to be controlled for load balancing between the worker edge servers. Decide.

The terminal control result collection module collects and manages the connection result context transmitted by the worker edge server 100, and reflects the connection decision between the worker edge servers.

The terminal control result monitoring module monitors the terminal control result of the corresponding worker edge server.

4 shows context classification according to elements involved in a collaboration system between edge servers based on resource prediction.

In the intelligent video security environment, in the collaboration system between edge servers based on computing resource prediction, the context is classified into worker edge server-related context, analytical intelligence context, terminal-related context, and collaboration result-related context between edge servers, depending on where the context is extracted.

Contexts related to worker edge servers are classified into network context, computing resource context, analytical intelligence context, and connected terminal context.

The network context consists of the IP address context representing the worker edge server. Computing resource context is a context for running analytics and consists of CPU, RAM, and storage usage. The analytical intelligence context is classified into an analysis target context and a required computing resource context according to the analysis target. The analysis target context consists of a security risk context that can be recognized by analytical intelligence. Required computing resources are composed of CPU, RAM, and storage contexts according to security risk perception.

Contexts related to connected terminals are classified into network context, location context, and video context. A network context is composed of a unique IP address possessed by a terminal. The location context is composed of latitude and longitude of the terminal based on GPS information. The video context consists of the quality, bit rate, and analysis target of the video transmitted from the terminal 300 to the worker edge server 200.

The context of collaboration results between edge servers is classified by the difference in analysis target and computing resource (CPU share, RAM share, storage usage). The analysis target is composed of the context of the target captured by the controlled terminal. The difference in computing resources is the difference between predicted computing resources and actually used computing resources, and is composed of contexts for CPU, RAM, and storage.

5 shows an example of predicting usage of computing resources according to terminal connection control in a worker edge server.

In an intelligent video security environment in which a master edge server 100, a plurality of worker edge servers 200, and a plurality of terminals 300 exist, the camera images of the terminal 300 (smartphone video, CCTV video, car black box) images, drone camera images) are transmitted to the Worker Edge Server 200, the Worker Edge Server 200 analyzes the images by analytical intelligence, and the Worker Edge Server 200 uses computing resources (CPU share, RAM). Occupancy, storage usage) is calculated.

The usage of computing resources (CPU usage, RAM usage, storage usage) of the worker edge server 200 is determined according to images transmitted from one or more terminals connected to the worker edge server. Depending on the quality (bit rate) of the video transmitted by the connected terminal and the object detected by the terminal, the amount of computing resources used to recognize security risks is different.

The master edge server 100 predicts the usage of computing resources according to the terminal connection control to balance the load between the worker edge servers when the number connected to the terminal 300 or the object recognized by the terminal changes. However, the computing power (CPU share, RAM share, storage use) of different worker edge servers is different. Accordingly, computing resources according to the connection control of the terminal are predicted differently. The terminal 300 selected for load balancing between the worker edge servers 200 is not a terminal newly connected to the worker edge server 200, but a worker under the control of the terminals 300 connected to the worker edge server 200. It is selected by the master edge server 100 that receives the standardized context by estimating the amount of computing resource usage (CPU share, RAM share, storage amount) of the edge server 200.

Figure 6 shows a collaboration decision flow chart of the autonomous collaboration system between edge servers based on computing resource prediction.

) 및 엣지서버들의 평균 컴퓨팅 리소스(

)를 계산한다. The Master Edge Server 100 performs linkage between the Worker Edge Servers 200 according to the change in the number of terminals connected to the Worker Edge Server 200 or the change in the analysis object being detected by the terminal. The master edge server 100 determines the improvement of the load balance according to the connection between the worker edge servers according to the change of the terminal-related context. The master edge server 100 calculates the computing resource estimate according to the terminal connection to determine the need for linkage between the worker edge servers (

) and average computing resources of edge servers (

) is calculated.

When the predicted computing resource value of the worker edge server is lower than the computing resource threshold value (Th), the master edge server 100 does not perform linkage between the worker edge servers because the load of the worker edge server is low.

Even if the predicted value of the computing resource usage of the worker edge server 200 is higher than the threshold value Th, if it is lower than the computing resource usage of the worker edge servers, load balancing cannot be performed according to the connection between the worker edge servers. Therefore, the master edge server 100 does not perform terminal connection control.

) 및 연결 종료(

)에 따라 계산된다. Worker 엣지서버의 단말 연결 제어에 따른 컴퓨팅 리소스의 변화를 계산한 이후 Worker 엣지서버간 부하 균형을 판단한다. Worker 엣지서버간 부하 균형은

가 작을수록 부하 균형의 향상도가 높은 것을 나타낸다. 뿐만아니라 단말 연결 제어에 따른 컴퓨팅 리소스의 사용량의 예측과 실제 사용 컴퓨팅 리소스 사용량으로 계산되는 신뢰도

를 이용한다. 신뢰도

는 컴퓨팅 리소스 사용량의 예측과 실제 사용 리소스 사용량의 차이가 적을수록 높은 값을 가지며, 예측의 정확도를 나타낸다. 또한, 단말 연결 제어에 따른 신뢰도의 변동을 줄이기 위해 EWMA(Exponential Weight Moving Average)를 사용하여

과 같이 계산한다.

가 낮아 부하 균형의 향상이 높더라도 신뢰도가 낮으면 선택된 협업 대상 및 단말이 선택되지 않는다. After determining the need for linkage between worker edge servers, the master edge server 100 determines linkage according to a stochastic collaboration model in order to reduce unnecessary collaboration between worker edge servers. The probabilistic collaboration model is calculated based on the predicted value of the computing resource amount and computing resource usage of the worker edge server, and is determined by comparing with a random value (R). The closer the ratio of the amount of computing resources of each worker edge server to the predicted value of computing resource usage is close to 1, the higher the probability of collaboration between edge servers. The control terminal and collaboration target for collaboration between worker edge servers are determined based on the change in computing resources of the worker edge server according to terminal connection control and the reliability of the usage of computing resources. The change in computing resources according to the terminal connection control of the worker edge server creates a connection between the terminal and the worker edge server (

) and end the connection (

) is calculated according to After calculating the change in computing resources according to the terminal connection control of the worker edge server, the load balance between the worker edge servers is determined. Load balancing between worker edge servers

It indicates that the degree of improvement of the load balance is so high that is small. In addition, the prediction of computing resource usage according to terminal connection control and the reliability calculated by actual computing resource usage

Use Reliability

has a higher value as the difference between the prediction of the computing resource usage and the actual resource usage is smaller, and indicates the accuracy of the prediction. In addition, EWMA (Exponential Weight Moving Average) is used to reduce reliability fluctuation according to terminal connection control.

Calculate as

Even if the improvement of load balance is high because is low, if the reliability is low, the selected collaboration target and terminal are not selected.

를 계산하고, 상기 신뢰도가 높은 경우 Worker 엣지서버의 부하 균형을 이루도록 컴퓨팅 리소스의 사용량이 적은 Worker 엣지 서버로 선택된 단말 연결 제어를 하도록 결정하면, 해당 Worker 엣지 서버가 선택된 단말과 연결 제어를 통해 자율협업을 한다. The master edge server has reliability based on the context shared by the plurality of worker edge servers.

, and if the reliability is high, if it is determined to control the connection of the selected terminal to the worker edge server with low computing resource usage to balance the load of the worker edge server, the corresponding worker edge server performs autonomous collaboration through connection control with the selected terminal. do

In an intelligent video security environment, the collaboration system between edge servers based on computing resource prediction periodically shares the result context of connection with terminals, Master and Worker edge servers, and analytical intelligence-related contexts for intelligent video security, and connects terminals using the shared context. Autonomous collaboration between edge servers is performed based on prediction of computing resources according to control.

Contexts are classified for each element involved in the autonomous collaborative linkage, and load balancing is performed based on the classified context characteristics, so that the distribution of computing resources of the entire worker edge server is minimized. When the connection control between the worker edge server and the terminal to perform collaboration is determined, the corresponding worker edge server and terminal determined from the master edge server are connected, and the load of each worker edge server's computing resources (CPU share, RAM share, storage usage) There is an effect of performing autonomous collaboration by providing load balancing by minimizing the occurrence of the situation and increasing the efficiency of intelligent video security service through real-time video analysis.

Through adaptive context sharing for intelligent video security and an autonomous collaboration system between edge servers, a video security environment where a master edge server and multiple worker edge servers and terminals exist (smartphone video, CCTV video, car black box video, drone video) Intelligent video security service based on real-time video analysis by minimizing unnecessary context sharing between a master edge server and multiple worker edge servers in camera video) and performing autonomous collaboration by providing load balancing of computing resources of worker edge servers expected to increase the efficiency of

Embodiments according to the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer readable recording medium. The computer readable recording medium may include program instructions, data files, and data structures alone or in combination. Computer-readable recording media include storage, hard disks, magnetic media such as floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - A hardware device configured to store and execute program instructions in storage media such as magneto-optical media, ROM, RAM, flash memory, etc. may be included. Examples of program instructions may include those produced by a compiler and machine language codes as well as high-level language codes that can be executed by a computer using an interpreter. The hardware device may be configured to operate as one or more software modules to perform the operations of the present invention.

As described above, the method of the present invention is implemented as a program and can be read using computer software on a recording medium (CD-ROM, RAM, ROM, memory card, hard disk, magneto-optical disk, storage device, etc.) ) can be stored in

Although described with reference to specific embodiments of the present invention, the present invention is not limited to the same configuration and operation as the specific embodiments to illustrate the technical idea as described above, and within the limit that does not deviate from the technical spirit and scope of the present invention It can be implemented with various modifications, and the scope of the present invention should be determined by the claims described later.

100: Master Edge Server
200: Worker Edge Server
300: Terminal

Claims (11)

a terminal having a wireless communication unit;
Equipped with analytical intelligence that receives and analyzes images from the terminal, controls terminal connection, extracts and formalizes the context of the worker edge server in real time, and periodically transfers the adaptively standardized context to the master edge server to the master edge server. A number of worker edge servers that transmit; and
Contexts shared from the plurality of worker edge servers are collected and stored, reliability is calculated, and when the reliability is high, terminal connection control is performed to the worker edge server with low computing resource usage to balance the load of the worker edge server. A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment, including a master edge server that controls the worker edge server to autonomously collaborate through connection control with the selected terminal. According to claim 1,
The terminal having the wireless communication unit
A smartphone equipped with a camera, GPS receiver, and wireless communication unit (WiFi, LTE 4G/5G communication unit, IoT communication unit), a CCTV camera equipped with a camera, GPS receiver, and wireless communication unit, and a black box equipped with a camera, GPS receiver, and wireless communication unit. A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment using any one of a vehicle equipped with a camera and a drone equipped with a GPS receiver and a wireless communication unit. According to claim 1,
The computing resource of the worker edge server is a collaboration system between edge servers based on computing resource prediction in an intelligent video security environment including CPU occupancy, RAM occupancy, and storage usage. According to claim 1,
The worker edge server includes analytical intelligence for analyzing images received from terminals,
A context extraction module for classifying and extracting contexts from terminals, worker edge servers, and the analytical intelligence for autonomous collaboration between worker edge servers, and transmitting the classified and extracted contexts to a context formalization module;
a context formalization module for standardizing the classified and extracted context;
a context sharing module that periodically transmits the standardized context to a master edge server;
a terminal control module that performs autonomous collaboration determined by the master edge server; and
A linkage result sharing module used for linkage between edge servers by transmitting a context created according to autonomous collaboration between worker edge servers to the master edge server and transmitting a linkage result to the master edge server;
A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment that includes According to claim 1,
The Master Edge Server
A context collection module that collects, stores, and manages multiple contexts received from multiple worker edge servers;
a real-time context monitoring module for checking the context collected by the context collection module using a web page;
Based on the multi-type context information received from the context collection module, if the number of terminals connected to the worker edge server changes or the object recognized by the terminal changes, connection control predicts the computing resource usage of the corresponding worker edge server according to the terminal resource prediction module;
An autonomous cooperation decision module for determining a worker edge server to be controlled and a terminal to be controlled for load balancing between worker edge servers using the computing resource prediction value received from the connection control resource prediction module;
A terminal control result collection module that collects and manages the connection result context transmitted by the worker edge server and uses it to determine the connection between edge servers; and
Terminal control result monitoring module for monitoring the terminal control result of the corresponding Worker edge server;
A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment that includes According to claim 1,
In the intelligent video security environment, in the collaboration system between edge servers based on computing resource prediction, the intelligent video security environment is classified into worker edge server-related context, analytical intelligence context, terminal-related context, and collaboration result-related context, depending on where the context is extracted. Collaboration system between edge servers based on computing resource prediction in According to claim 6,
The worker edge server related context is classified into a network context, a computing resource context, an analytical intelligence context, and a connected terminal context.
The network context is composed of an IP address context representing a worker edge server,
The computing resource context is a context for executing analytical intelligence and is composed of CPU, RAM, and storage usage.
The analysis intelligence context is classified into an analysis target context and a required computing resource context according to the analysis target, the analysis target context is composed of a security risk context in which analytical intelligence can recognize, and the required computing resource is a CPU according to security risk recognition; Consists of RAM and storage context,
The context related to the connected terminal is classified into a network context, a location context, and a video context, the network context is composed of a unique IP address possessed by the terminal, and the location context is composed of the latitude and longitude of the terminal based on GPS information, , The video context consists of the quality (bit rate) of the video transmitted from the terminal to the worker edge server, and the analysis target,
The context of the collaboration result between the edge servers is classified by the difference between the analysis target and the computing resource (CPU share, RAM share, storage usage),
The analysis target consists of the context of the target captured by the controlled terminal, and the difference in computing resources is the difference between the predicted computing resource and the actually used computing resource, which consists of the context of CPU, RAM, and storage, intelligent video security. Collaboration system between edge servers based on prediction of computing resources in the environment. According to claim 1,
In an intelligent video security environment in which the master edge server, the plurality of worker edge servers, and the plurality of terminals exist, the camera video (smartphone video, CCTV video, car black box video, drone camera video) of the terminal is displayed as The worker edge server analyzes the image by analytical intelligence, calculates the computing resource usage (CPU share, RAM share, storage usage) of the worker edge server, and computes the computing resource of the worker edge server. Resource usage (CPU share, RAM share, storage usage) is determined according to the video transmitted from one or more terminals connected to the worker edge server, and the quality (bit rate) of the video transmitted by the connected terminal and the object detected by the terminal are determined. Computing resource usage for perceiving security risks varies depending on the
The master edge server predicts the usage of computing resources according to terminal connection control to balance the load between the worker edge servers when the number connected to the terminal changes or the object recognized by the terminal changes, but the computing resources of different worker edge servers Usage (CPU occupancy, RAM occupancy, storage usage) computing power is different, and accordingly, computing resources according to terminal connection control are predicted differently,
The terminal selected for load balancing between the worker edge servers is not a terminal newly connected to the worker edge server, but the usage of computing resources (CPU share, RAM share) of the worker edge server according to the control of the terminals connected to the worker edge server. A collaboration system between edge servers based on prediction of computing resources in an intelligent video security environment, selected by the master edge server receiving a standardized context by predicting storage usage). According to claim 1,
The Master Edge Server performs linkage between Worker Edge Servers according to the change in the number of terminals connected to the Worker Edge Server or the change in the object of analysis that the terminal is detecting. The improvement of the load balance is determined according to the linkage between the edge servers, and the master edge server (computing resource prediction value according to the terminal connection (to determine the need for linkage between the worker edge servers)

) and average computing resources of edge servers (

) is calculated,
The master edge server does not perform linkage between worker edge servers because the load of the worker edge server is low when the computing resource prediction value of the worker edge server is lower than the computing resource threshold value (Th).
Even if the predicted value of the computing resource usage of the worker edge server is higher than the threshold value (Th), if it is lower than the computing resource usage of the worker edge servers, load balancing cannot be performed according to the connection between the worker edge servers. A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment that does not perform connection control. According to claim 1,
After determining the need for linkage between the worker edge servers, the master edge server determines linkage according to a stochastic collaboration model in order to reduce unnecessary collaboration between worker edge servers. Calculated based on the predicted value of usage, determined by comparing with a random value (R),
As the ratio between the amount of computing resources of each worker edge server and the predicted value of computing resource usage approaches 1, the probability of collaboration between edge servers increases. It is determined based on the change in the computing resource of the server and the reliability of the usage of the computing resource, and the change in computing resource according to the control of the terminal connection of the worker edge server creates a connection between the terminal and the worker edge server (

) and end the connection (

), and after calculating the change in computing resources according to the terminal connection control of the worker edge server, the load balance between the worker edge servers is determined, and the load balance between the worker edge servers is

The smaller the value, the higher the improvement in load balancing, as well as the reliability calculated from the prediction of computing resource usage according to terminal connection control and the actual usage of computing resources.

Using , the reliability

has a higher value as the difference between the prediction of computing resource usage and the actual computing resource usage is smaller, has the accuracy of prediction, and uses EWMA (Exponential Weight Moving Average) to reduce the fluctuation of reliability according to terminal connection control. So

Calculate as

A collaboration system between edge servers based on prediction of computing resources in an intelligent video security environment in which the selected collaboration target and terminal are not selected if the reliability is low even if the load balance is improved due to low . According to claim 10,
The master edge server determines the reliability based on the context shared by the plurality of worker edge servers.

, and if the reliability is high, if it is determined to control the connection of the selected terminal to the worker edge server with low computing resource usage to balance the load of the worker edge server, the corresponding worker edge server performs autonomous collaboration through connection control with the selected terminal. A collaboration system between edge servers based on computing resource prediction in an intelligent video security environment.

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