KR20230060211A - Lightweight KubeEdge configuration method and apparatus for edge computing environment - Google Patents

Abstract

In accordance with the present invention, disclosed are a method and an apparatus for configuring a lightweight cube edge for an edge computing environment. In accordance with the present invention, the method for configuring a lightweight cube edge system for an edge computing environment by using a lightweight cube edge tool on a cube edge system including a cloud core and an edge core, includes: a step in which the lightweight cube edge tool generates a virtual machine by using a kernel virtual machine (KVM); a step in which all nodes including a master node on the side of the cloud core and a plurality of edge nodes on the side of the edge core installs a container and sets a container runtime; a step in which the master node generates a master node token; a step in which the plurality of edge nodes transmits the master node token to a worker node; and a step in which the worker node forms a cluster by combining the master node token to the master node. Therefore, the present invention is capable of constructing a lightweight edge cube system without a complex setting procedure.

Description

Lightweight KubeEdge configuration method and apparatus for edge computing environment}

The present invention relates to a lightweight cube edge configuration method and apparatus for an edge computing environment.

With the rapid increase in the number of mobile devices, service providers are increasingly adopting edge computing to adapt to large amounts of data with low latency requirements when launching their services.

Application developers are moving toward configuring Kubernetes at the edge through open source projects such as KubeEdge, Akri, and Google Anthos, along with large cloud providers (Microsoft Azure, Amazon Web Services, etc.).

Since Kubernetes is not suitable for edge computing, KubeEdge, a lightweight version of Kubernetes, has been proposed.

CubeEdge allows operators to deploy and manage services with containerization on the edge side through a centralized management interface. In order to distribute services in CubeEdge systems, services must be designed in CubeEdge systems.

Therefore, an easy and fast CubeEdge system configuration solution is required to accelerate service development, but many complicated configuration processes are required for CubeEdge installation.

US Patent Publication 2021-0232498

In order to solve the problems of the prior art, the present invention proposes a lightweight cube edge construction method and apparatus for an edge computing environment capable of automatically configuring a cube edge system.

In order to achieve the above object, according to an embodiment of the present invention, a lightweight cube edge system for an edge computing environment is configured using a lightweight cube edge tool for a cube edge system including a cloud core and an edge core As a method, the lightweight cube edge tool, using a kernel-based virtual machine (Kernal Virtual Machine, KVM) to create a virtual machine and build a networking environment; installing containers and setting container runtimes at all nodes including a master node on the cloud core side and a plurality of edge nodes on the edge core side; Generating, by the master node, a master node token; transmitting, by the plurality of edge nodes, the master node token to a worker node; and combining the worker node with the master node using the master node token to form a cluster.

In the step of establishing the networking environment, the lightweight cube edge tool sets up a LAN for forming the cloud core-side Kubernetes cluster and a LAN for connecting the Kubernetes cluster and the plurality of edge nodes. can include

Prior to the step of installing containers in all nodes, the lightweight cube edge tool may further include inactivating swap memory for all nodes.

All the nodes can set Cgroup drivers, which are defined as mechanisms for controlling specific subsystems of the kernel, including devices, CPU, RAM, network access, and the like, to the container runtime.

The master node may be a Kubernetes API server.

Before the step of generating the master node token, the master node may further include installing Kubernetes and Cube Edge.

Prior to the step of transferring the master node token to the worker node, the plurality of edge nodes may further include installing a cube edge.

According to another aspect of the present invention, a computer readable recording medium storing a program for performing the above method is provided.

According to another aspect of the present invention, a lightweight cube edge system configuration device for an edge computing environment including a cloud core and an edge core, comprising: a processor; And a memory connected to the processor, wherein the memory creates a virtual machine using a Kernel Virtual Machine (KVM) and builds a networking environment, and the cloud core-side master node and the edge All nodes, including a plurality of edge nodes on the core side, install containers, all nodes set container runtimes, and the master node generates master node tokens. A lightweight cube-edge system configuration device that stores program instructions executed by the processor so that the node token is transferred to the worker node, and the worker node combines with the master node using the master node token to form a cluster. Provided.

According to the present invention, there is an advantage of automatically building a lightweight cube edge system without a complicated setting process.

1 is a diagram showing the detailed configuration of a general cube edge system.
2 is a diagram for explaining a process of configuring a lightweight cube edge system according to the present embodiment.

Since the present invention can make various changes and have various embodiments, specific embodiments are illustrated in the drawings and described in detail.

However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

The CubeEdge system is an open-source edge computing platform designed to extend Kubernetes containerized orchestration capabilities to hosts at the edge, enabling network application deployment and metadata synchronization between cloud and edge devices.

1 is a diagram showing the detailed configuration of a general cube edge system.

Referring to FIG. 1 , the CubeEdge system includes a CloudCore 100 and an EdgeCore 102 .

The cloud core 100 is an extended Kubernetes controller that controls metadata of edge nodes and pods to transmit data to the identified edge nodes.

The cloud core 100 may include an edge controller 110 , a device controller 112 , and a cloud hub 114 .

The edge controller 110 manages a plurality of edge nodes (devices) existing in an edge cluster.

To this end, the edge controller 110 connects the Kubernetes API server (K8s API Server, master node, 130) and a plurality of edge nodes, and registers the plurality of edge nodes with the Kubernetes API server 130 through this. .

The device controller 112 manages metadata/states of edge nodes in the edge cluster, and manages the edge nodes through synchronization between the edge and the cloud.

The cloud hub 114 provides a communication function between the cloud core 100 and the edge core 102, particularly between the edge controller 110 and the edge node.

The Edge Core 102 may include an Edge Hub 120 , a MetaManager 122 , an EdgeD 124 , and a Device Twin 126 .

The edge hub 120 interacts with the cloud core 100 for edge computing, and provides a communication function between the cloud core 100 and the edge core 102 to report the state of the edge node to the cloud side and to transfer resources between them. Synchronize updates.

The meta manager 122 manages the state of containers of edge nodes managed through the edge D 124 . To this end, the meta manager 122 exchanges metadata with the device controller 112 .

EdgeD 124 manages the containers of the Edge node and operates Pods in the CubeEdge system.

Every edge node must have Kubelet, an agent that runs containers. Edge nodes are registered with the Kubernetes API server 130 through the Edge Hub 120 and the Cloud Hub 114. Kubelet receives tasks to be performed on each edge node, such as starting, stopping, and terminating application containers, from the Kubernetes API server 130. It also sends edge node and container state back to the Kubernetes API server (130).

The device twin 126 stores the state of the edge node and manages the resource state.

The device twin 126 collects the resource state of each edge node through the mapper 140, receives the collected resource state through the event bus 142, and stores it in its own DB.

Event bus 142 is an MQTT client that interacts with the MQTT server, providing pub/sub functionality to other components.

Thereafter, the resource state is transmitted to the device controller 112 through communication between the edge hub 120 and the cloud hub 114, and synchronization is performed so that the same content can be stored and managed on the cloud side and the edge side.

The Service Bus (144) receives a message from a user who wants to utilize edge node resources and forwards it to the Edge D (124) through the Meta Manager 122 so that the container can perform the task requested by the user. .

Edge nodes can include Docker, container runtime interfaces, and container runtimes.

These configurations are described in detail below.

CRI (Container Runtime Interface) is a plug-in interface that provides kubelet with the ability to use various container runtimes. Kubernetes plugins also use CRI to launch and observe containers. CRI includes the gRPC API, protocol buffers and libraries.

The Open Container Initiative (OCI) runtime is a low-level runtime. The low-level runtime performs container life cycle management, creates and runs containers.

Containerd is the default CRI of Docker Engine, and Containerd provides minimal functionality for running containers and managing images and snapshots on nodes. It executes and terminates the container by delegating the execution task to the OCI runtime. Containerd does the following to launch a new container:

CRI-O is a container runtime built to provide a link between the high-level Kubernetes CRI and OCI runtimes.

Docker uses Containerd as the default container runtime.

You can use the container runtime to create and manage containers. However, you need tools to manage and orchestrate these containers on a clustered system. Container orchestration tools are used for container lifecycle management because a service is typically composed of multiple containers.

You can use a variety of container orchestration tools such as Kubernetes, Apache Mesos, Docker Swarm, and more.

Typically, containers are deployed to hosts in replicated groups called pods. Container orchestration tools schedule deployments, and based on predefined constraints such as idle compute units, available memory, network bandwidth, etc., the most appropriate host is chosen to deploy the container.

While containers run on the host, container orchestration tools manage the container's lifecycle. Container orchestration tools can be used in any environment that can run containers, from personal computers to private cloud instances running on Amazon Web Services (AWS), DigitalOcean, and more.

Docker Engine is an open source containerization platform based on Linux containers for building and containerizing applications. It extends current container technology by providing lightweight containers and APIs for managing container images in multi-container applications.

Docker Engine can help deploy applications from one environment to another, regardless of the environment setup, and any network function in the service function chain can be created as a container.

Docker containers depend on the kernel of the host running Docker Engine. It separates user space only at the operating system level and consumes resources when running applications. Basically, a container consists of a process that is isolated from the rest of the process by a namespace. Containers running on Docker share the host OS kernel. It builds containers on top of the operating system using Linux kernel features such as storage, networking, and control groups. Docker bundles an application's software into a package that contains everything it needs to run, such as the OS, application code, runtime, system tools, and libraries. Therefore, applications can run on other Linux systems regardless of custom configurations. It also creates a Docker container by adding a read-write file system on top of the read-only file system of the Docker image.

2 is a diagram for explaining a process of configuring a lightweight cube edge system according to the present embodiment.

Referring to FIG. 2 , the steps to be performed in both the cloud core and the edge core and the steps to be performed in the individual core are distinguished through the lightweight cube edge tool according to the present embodiment.

The lightweight CubeEdge tool according to this embodiment may be defined as a program command executed in a device including a processor and memory.

The processor may include a central processing unit (CPU) capable of executing a computer program or other virtual machines.

The memory may include a non-volatile storage device such as a fixed hard drive or a removable storage device. The removable storage device may include a compact flash unit, a USB memory stick, and the like. The memory may also include volatile memory such as various random access memories.

The memory stores program instructions for configuring the lightweight cube-edge system.

apply to all nodes

The following process is performed in both nodes included in the cloud core and nodes included in the edge core.

The lightweight CubeEdge tool according to this embodiment creates a virtual machine (VM) using a kernel-based virtual machine (Kernal Virtual Machine, KVM) (step 200).

After creating a virtual machine, the lightweight CubeEdge tool establishes a networking environment by setting at least two LANs inside the KVM (step 202).

One LAN is for forming the cloud-side Kubernetes cluster of the CubeEdge system, and the other is for connecting the cloud-side Kubernetes cluster and the edge nodes of the CubeEdge system.

Subsequently, network interfaces are added to each virtual machine, and network interfaces corresponding to the number of edge nodes are added to the Kubernetes API server 130 on the cloud side.

To prevent the cube-scheduler from assigning pods to nodes that are running out of CPU/memory or have reached their specified CPU/memory limits, you should disable the swap area on all nodes, both on the cloud core side and on the edge core side.

Since the CubeEdge system is based on Kubernetes, the lightweight CubeEdge tool disables swap memory for all nodes included in Cloud and Edge Core (step 204).

Then, all nodes install containers (step 206).

Each node in the CubeEdge system sets the Cgroup driver to the container's runtime (step 208).

Here, Cgroup is a mechanism for controlling a specific subsystem of the kernel including device, CPU, RAM, network access, and the like.

cloud node

Install Kubernetes (K8s) on the master node (step 210).

Here, the master node is the Kubernetes API server 130.

Then, Cube Edge is installed on the master node (step 212).

Establish a master node and have the master node generate a token (step 214).

Finally get the master node token (step 216).

edge node

CubeEdge is installed on the edge node (step 218).

Pass the master node token to the worker node (step 220).

Finally, the worker node joins with the master node using the master node token to form a cluster (step 222).

The embodiments of the present invention described above have been disclosed for illustrative purposes, and those skilled in the art having ordinary knowledge of the present invention will be able to make various modifications, changes, and additions within the spirit and scope of the present invention, and such modifications, changes, and additions will be considered to fall within the scope of the following claims.

Claims (10)

A method of constructing a lightweight cubeedge system for an edge computing environment using a lightweight cubeedge tool for a cubeedge system including a cloud core and an edge core,
The lightweight CubeEdge tool creates a virtual machine using a kernel-based virtual machine (KVM) and builds a networking environment;
installing containers and setting container runtimes at all nodes including a master node on the cloud core side and a plurality of edge nodes on the edge core side;
Generating, by the master node, a master node token;
transmitting, by the plurality of edge nodes, the master node token to a worker node; and
A lightweight cube edge system configuration method comprising the step of forming a cluster by combining the worker node with the master node using the master node token. According to claim 1,
The step of building the networking environment,
A lightweight cube edge system configuration method comprising the step of setting, by the lightweight cube edge tool, a LAN for forming the cloud core-side Kubernetes cluster and a LAN for connecting the Kubernetes cluster and the plurality of edge nodes. . According to claim 1,
The lightweight cube edge system configuration method further comprising deactivating, by the lightweight cube edge tool, swap memory for all the nodes before the step of installing containers in all nodes. According to claim 1,
All the nodes set a Cgroup driver, which is defined as a mechanism for controlling a specific subsystem of the kernel, including devices, CPU, RAM, network access, etc., to the container runtime. According to claim 1,
The method of configuring a lightweight cube edge system in which the master node is a Kubernetes API server. According to claim 1,
Prior to the step of generating the master node token, the lightweight cube edge system configuration method further comprising the step of installing, by the master node, Kubernetes and cube edge. According to claim 1,
Prior to the step of transmitting the master node token to the worker node, the plurality of edge nodes further comprising the step of installing a cube edge. A computer-readable recording medium in which a program for performing the method according to claim 1 is stored. A lightweight cube edge system configuration device for an edge computing environment including a cloud core and an edge core,
processor; and
Including a memory coupled to the processor,
the memory,
Create a virtual machine using a kernel-based virtual machine (KVM) and build a networking environment,
All nodes including a master node on the cloud core side and a plurality of edge nodes on the edge core side install containers;
allow all the nodes to set up a container runtime;
cause the master node to generate a master node token;
allowing the plurality of edge nodes to pass the master node token to a worker node;
The worker node uses the master node token to combine with the master node to form a cluster,
A lightweight cube edge system configuration device for storing program instructions executed by the processor. According to claim 9,
The program instructions are lightweight cube edge system configuration device for inactivating the swap memory for all the nodes before all the nodes install the container.

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