Case study for ZCITC Streamlining Urban Parking with KubeEdge (ENG)

src/pages/case-studies/zcitc/index.mdx

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 # ZCITC practice in smart parking base on KubeEdge
 
-For more detailed information, please visit http://kubeedge.io/zh/case-studies/zcitc.
+As cities continue to integrate diverse parking resources and expand application scenarios, edge applications are becoming increasingly complex.
+The number of edge nodes is growing rapidly, generating massive data, and presenting challenges for managing edge applications. The main challenges include:
+
+(1) Complex network topology and unstable network access. During the transformation of traditional parking lots into smart facilities and the construction of 
+new intelligent parking lots, the network planning depends on project requirements, physical locations, and road layouts. Large parking lots with fiber optic 
+conditions can use dedicated lines, while others must rely on mobile networks due to physical constraints. The network quality varies, and municipal construction 
+can occasionally cause power and network outages, impacting system availability.
+
+(2) Highly customized requirements and rapid iteration updates. Cities host a variety of parking resources, such as public street parking, commercial lots, 
+residential lots, and shared spaces. Each parking lot has unique technologies, operators, and fee policies, making a single application version insufficient. 
+Even after construction, applications require updates to accommodate operations like promotional discounts during events. These factors demand excellent version control, 
+scalability, and elastic deployment capabilities.
+
+(3) Challenges of heterogeneous edge gateway hardware. Parking lot sizes and computational demands vary significantly. Small lots may only have one entry/exit and a few spaces, 
+while larger lots have multiple zones and unique pricing policies. These differences lead to diverse hardware requirements, including x86_64, aarch32, and aarch64 CPU architectures, 
+adding complexity to the technical stack and increasing management challenges for development teams.
+
+Zhejiang Zhicheng Software Co., Ltd. specializes in city-wide smart parking solutions, leveraging big data for intelligent parking resource integration. 
+To address the needs of unattended parking lots for high security, reliability, efficiency, and maintainability, the Zhicheng team increasingly deploys 
+containerized applications on edge nodes at parking lots.
+
+# Cloud-Native Edge Computing Technology
+
+Edge computing is a distributed architecture that processes data closer to the user, reducing latency and improving transmission speeds compared to cloud computing. 
+Cloud-native methods build and run applications using open-source technologies like Kubernetes and Docker. They rely on microservice architectures, 
+agile methodologies, and DevOps practices to enable flexibility, scalability, and automation.
+
+As computing demands grow, combining cloud-native and edge computing creates a new technical trend: cloud-native edge computing. 
+This approach brings cloud capabilities to edge devices, enabling offline operations, edge-cloud collaboration, and massive device connectivity. 
+KubeEdge is a leading solution implementing this paradigm, extending Kubernetes’ capabilities to the edge.
+
+![KubeEdge Architecture](images/kubeedgeArchitecture.png)
+
+# Smart Parking OS Based on Cloud-Native Architecture
+
+## Overall Architecture
+
+The integrated smart parking platform combines on-street and off-street parking resources, fuses static and dynamic traffic data, 
+and supports multiple operators to create an open ecosystem. It provides operational services and solutions for parking managers and users. 
+Using a cloud-native architecture, Zhicheng containerized all parking lot services, deploying them either in the cloud or on edge nodes as needed. 
+Together, the cloud services, on-site sensors, control devices, and edge applications form a parking cloud service system.
+
+![System Architecture](images/systemStructure.png)
+
+## Technical Implementation
+
+Kubernetes offers a model for cloud-native application orchestration, while KubeEdge extends these capabilities to the edge. This ensures support 
+for complex edge scenarios and high availability. Below is an overview of the relationship between Kubernetes clusters and their components.
+
+![Kubernetes Cluster Structure](images/KubernetesCluster.png)
+
+The edge side of parking lots comprises loosely coupled containerized applications leveraging Kubernetes and KubeEdge to meet functional demands.
+
+![Edge Architecture](images/EdgeArchitecture.png)
+
+Key applications deployed on the edge include:
+
+(1) **Core Service Group:** Uses Postgres for data storage, Redis for caching to reduce disk I/O, Zabbix for physical machine monitoring, 
+and Zeroconf Service for gateway discovery for user interfaces like toll booths.
+
+(2) **Gateway Broker:** Provides message subscription, publication, and bi-directional communication with the cloud platform.
+
+(3) **Device Integration Applications:** Independent services for cameras, gates, display screens, voice announcements, and vehicle detectors. 
+These services are low-coupled, allowing flexible deployment.
+
+(4) **Storage and Upload Applications:** Manage local storage and upload of sensor data like images and videos, monitor disk usage, 
+and perform periodic data cleanup.
+
+(5) **Business Applications:** Handle vehicle entry/exit, order generation, channel control, and payment calculations.
+
+### Functional Features:
+
+1. **Edge Database Master-Slave Configuration:**
+   For large-scale parking lots, dual or multiple machine hot-standby solutions are implemented. PostgreSQL and Redis applications use StatefulSet mechanisms for stable re-scheduling.
+
+![Master-Slave Configuration](images/MasterSlave.png)
+
+2. **Multi-Active Redundancy:**
+   Kubernetes ReplicaSet ensures multiple application replicas are active simultaneously for load balancing and high availability.
+
+3. **Node Deployment of Related Applications:**
+   Parking management applications and device control services are co-deployed to optimize communication and response times.
+
+4. **Cross-Node Deployment of Application Instances:**
+   Instances of the same application are deployed across nodes to improve availability.
+
+5. **Grouped Deployment Based on Edge Node Properties:**
+   Applications are deployed based on edge node labels for different CPU architectures or device types.
+
+6. **Automatic Deployment of New Nodes:**
+   Newly added nodes are labeled, and applications are automatically deployed.
+
+7. **Inter-Linked Parking Lots:**
+   Complex scenarios requiring communication between parking lots use KubeEdge’s EdgeMesh for lightweight, integrated service discovery.
+
+## Results
+
+Currently, Zhicheng's smart parking operating system has implemented application containerization, fully adapted to the computing platform of x86_64, aarch32, 
+and aarch64 CPU architecture, elastically adapting to parking lot scale and construction costs. Gateway devices use lightweight MQTT protocol for message communication,  
+achieving low consumption and stability while greatly reducing bandwidth pressure. Kubernetes' container orchestration capabilities bring rapid deployment and high availability to 
+parking lot systems.
+
+In practice, implementation teams can deploy one or several edge gateway devices with appropriate computing power as edge computing platforms according to parking lot size and turnover rate, 
+following a "one block, one strategy, one solution" approach. Edge computing provides the following capabilities for the cloud platform:
+
+(1) Intelligent access to parking lot devices. Achieves collection and reporting of sensor data from entrance/exit cameras and vehicle detectors, and networked control of barriers, 
+space display screens, voice broadcasting, and other control units.
+
+(2) Two-way synchronization of business data. Real-time communication with the cloud, receiving basic information like parking lot, channel, and vehicle whitelist information from the cloud; 
+pre-processing vehicle entry/exit information, generating parking orders, and calculating fees based on charging schemes. Business data has two-way synchronization capability while ensuring data consistency.
+
+(3) Short-term offline working capability. Provides limited services to drivers during short disconnections, such as barrier opening for entry, whitelist and free order barrier opening for exit, 
+and offline parking fee calculation. Data automatically synchronizes when network connection is restored to maintain cloud and edge data consistency.
+
+(4) Load balancing and high availability. Elastic deployment, with some critical businesses meeting "single-point multi-active" and "cloud-edge" multi-site active scenarios, 
+providing load balancing and high availability for parking lot business.
+
+(5) Continuous delivery, rapid deployment, and application updates. Fully containerized applications, combined with Kubernetes and KubeEdge orchestration deployment capabilities, 
+achieve rapid iteration and continuous delivery of parking lot business, with automated deployment, update, and rollback operational management capabilities for edge nodes.
+
+## Conclusion
+
+Currently, unmanned fee collection services and online operations are in the early blue ocean market and provide core value of cost reduction and efficiency improvement for users. In the future, 
+Zhicheng will continue to focus on IoT, edge computing, and other technologies to provide intelligent operation capabilities for parking lots, dedicated to improving parking space turnover and occupancy rates, 
+reducing labor costs, and thereby increasing parking lot operational benefits.