6 Pain Points of Traditional Internet of Vehicles Architecture: IoV Series (I)

In the past two years, the development of the Internet of Vehicles (IoV) technology has been widely publicized and actively promoted by governments, research institutes and major Internet giants. IoV is mainly applied in two ways: before assembly and after assembly. In the pre-assembly stage, the vehicle is equipped with an IoV device before leaving the factory, which is dominated by the vehicle manufacturer. This may also include the collaboration between a vehicle manufacturer and an IoV solution provider, such as the collaboration between SAIC and Alibaba. In the post-assembly stage, typically the IoV device is connected to the OBD port of the vehicle. The IoV solution uses an intelligent terminal (that is, an IoV device) to collect all raw data from the CAN bus of the OBD port, diagnoses and analyzes the data, records driving information, resolves the data (various sensor values from the electronic control system), and outputs the data through the serial port for users to access, analyze, and use. The vehicle data collected by the IoV device is presented to the IoV mobile app.

Characteristics of the IoV Industry

  1. The number of monthly active IoV devices is large and they keep online for a long time. Today, vehicles are essential for people to travel. Once a vehicle is on the road, the IoV device goes online, collects and reports data to the IoV platform. The average time for a IoV device online is 3 hours per day, depending on the congestion of the city.
  2. The peaks of travel in the morning and evening are fixed. A regular feature of the IoV industry is that the peaks of travel in the morning and evening are concentrated. The morning peaks are concentrated between 6:00 am and 9:00 am, and the evening peaks are concentrated in the 3 hours from 17:00 pm to 20:00 pm. This results in a peak traffic flow of around 6 hours a day. How to deal with the morning and evening peaks at a lower cost is a realistic problem.
  3. The holiday peak traffic is difficult to predict. Due to the free policy of the expressway during the national legal holidays, more and more people are choosing to drive or travel. Therefore, whenever a holiday comes, it will inevitably lead to a surge in the number of IoV users, and the time and flow of the peak traffic are uncertain. How to accurately predict the peak travel time of each holiday is a problem.
  4. High concurrency, high capacity, and complex scenarios. The number of monthly active IoV devices is large. The peaks appear in the morning and evening, resulting in high concurrency. The IoV devices keeping online for an average of 3 hours per day produce a huge amount of data, which causes the data to be written is much more than that to be read during data collection. However, the data to be read is less than that to be written in group communication with social media, friend circles, and vehicle driving reports. The complex application scenarios have high requirements for application architecture.
  5. The speed of automotive technology updating is fast. Nowadays, automobile technology updating is getting faster and faster, more and more automobile manufacturers are emerging, and the frequency of new models released by manufacturers is getting higher and higher. IoV enterprises must maintain high attention to new technologies in the automobile industry, speed up version iteration, and improve R&D efficiency to quickly respond to changes in the automotive market and meet the market demand.

Increasing Cloud Adoption for IoV

Traditional in-house IDC solutions are difficult to achieve this goal, unless at a very high cost. In contrast, cloud computing can resolve these problems from all aspects. Therefore, migration to cloud is the best choice. However, there are many cloud computing vendors, including Chinese companies and foreign companies. Choosing the most suitable cloud computing vendor can be a challenging process, but through our investigation and analysis, we felt that Alibaba Cloud was the best choice according to our business needs.

After deciding on Alibaba Cloud, the next step is to consider how to migrate to the cloud. This article series aims to share some of the details of the migration process.

Traditional IoV Architecture

Business Architecture

IoV platform: consists of the application layer, support layer, and physical layer. The application layer implements user registration, user logon, navigation, vehicle friends, vehicle detection, track query, and other entertainment functions. These are the core functions of the app, followed by support layer functions, such as operations management systems, user management systems, vehicle management systems, and other auxiliary O&M systems and tools.

Capability resource platform: provides resources and capabilities for customers and partners as an open platform, for example, fleet services, data applications, and location services.

Third-party cooperation platform: provides insurances, violation inquiry, parking space searching, 4S shop services, and other functions by calling third-party platform interfaces.

Application Architecture

Data Streams

  1. Data Collection: The smart vehicle equipment collects driving data and reports the data to the platform through an IoT card (that is, SIM card). The platform converts the data into readable data through the protocol parsing service and stores the readable data and original data.
  2. Data Processing: After the parsed data is stored in MQ, application services at the back end start to process the data. For example, the trajectory service obtains trajectory data from MQ for analysis and processing, and generate users’ driving trajectory; the fault detection service identifies vehicle faults by subscribing to vehicle sensor values in MQ for analysis.
  3. Data Analysis: Some driving data is finally stored in the database through the processing of each module, and specific scenarios are analyzed using big data. For example, the driving behavior analysis service analyzes a user’s daily driving behaviors (for example, rapid acceleration, rapid deceleration, and sharp turn) to rate the user’s driving.
  4. Data Display: After downloading and installing the mobile app, a user can log on to the app to view the vehicle location, track, fuel consumption, and failures and enjoy functions such as making friends and entertainment.

Technical Details of Application Architecture


Server Load Balancer Cluster

The Layer-7 Server Load Balancer cluster uses Nginx servers, which provides load balancing and reverse proxy for back-end web application servers. In addition, Nginx supports regular expressions and other features.

The bottleneck is the expansion of IDC network bandwidth. You need to apply for the expansion, which takes 1 to 2 days from completing the internal process to the operator process. This undermines fast expansion of network bandwidth, making it unable to cope with sudden traffic growth. It is a waste of resources if you purchase a lot of idle bandwidth for a long time. After all, high-quality network bandwidth resources are quite expensive in China. As the company’s O&M personnel, how to help increase income and reduce expenditures and make every penny count is our responsibility, obligation, and more of an ability.

Application Server Cluster

Java: Centos7, JDK1.7, and Tomcat7

PHP: Centos7 and PHP5.6.11

Node.js: Centos7 and Node8.9.3

Currently, we use Java, PHP, and Python as application development languages and use Tomcat, Nginx, and Node.js as web environments. Application upgrades are released basically using scripts because application releasing is not highly automated. Applications are usually released and upgraded in the middle of the night, requiring a lot of overtime. Heavy repetitive O&M workload diminishes a sense of accomplishment. O&M engineers either solve problems or upgrade and release services most of the time. They do not have time to improve themselves. They become confused without orientation, which increases staff turnover. A vicious circle is inevitable if this problem fails to be solved.

Distributed Service Cluster

Alibaba Cloud Dubbo is an open source distributed service framework, which is also a popular Java distributed service framework. However, the lack of Dubbo monitoring software makes troubleshooting difficult. A robust link tracking and monitoring system improves distributed applications.

Cache Cluster

The biggest pain point with caching is O&M. Redis cluster failures are frequently caused by the disk I/O capability bottleneck, and Redis clusters need to be frequently resized online because of rapidly increasing users. In addition, Redis clusters must be operated and maintained manually, resulting in heavy workload and misoperation. Countless failures are caused by Redis clusters. The problem is also associated with strong dependency of applications: the entire application crashes when Redis clusters fail. That is an disadvantage of application system design.

Message Queue (MQ) Cluster

However, it has a serious pain point. As open source software, Kafka is associated with several previous failures. In Version 0.8.1, a bug exists in the topic deletion function. In Version 09, a bug exists in Kafka clients: when multiple partitions and consumers exist, a partition may be congested by rebalancing. Version 10 is different from Version 08 in the consumption mode so that we had to rebuild the consumption program. In summary, we have encountered too many failures caused by Kafka bugs. Small and medium-sized enterprises are unable to fix bugs of such software due to limited technical capabilities, which leaves them passive and helpless.

StreamCompute Cluster

Data Storage Cluster

A database cluster includes multiple databases, for example, MySQL cluster, MongoDB cluster, and Elasticsearch cluster.

MySQL Cluster

MongoDB Cluster

Elasticsearch Cluster

Distributed Network File System (NFS)

However, the self-built distributed NFS is barely scalable because of our limited investment in hardware devices. In addition, downtime is required, which seriously affects business. The access rate decreases as the number of clients increases. As a pain point impairing user experience, the problem must be solved.

O&M Control Cluster

Monitoring: The open source Zabbix monitoring system is used.

Code Management

Bastion Host

Log query and management: The open source ELK log system is used.

Continuous Integration

Configuration Management System

Although the current O&M system is standard, most O&M tools are open source services that can only meet part of function requirements. As O&M control requirements increase, we should be familiar with more and more open source services. As O&M management is inconsistent, O&M engineers usually should be familiar with many O&M systems, which makes it difficult for beginners.

Pain Points of Traditional IDC Architecture

Pain Point 1: O&M Is Insufficiently Automated, Resulting in Heavy and Redundant Workload

Pain Point 2: Without Auto Scaling Capabilities, We Have to Spend a Lot Dealing with Business Traffic Peaks

Pain Point 3: O&M Tools Are Scattered, and O&M Is Complex and Tedious

Pain Point 4: Hardware Devices Are Purchased in a Long Cycle with High Costs and Low Scalability

Pain Point 5: Infrastructure Is Unreliable with Frequent Failures

Pain Point 6: Without Adequate Security Protection Capabilities, the System Is Vulnerable to Attacks




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Alibaba Cloud

Follow me to keep abreast with the latest technology news, industry insights, and developer trends. Alibaba Cloud website:https://www.alibabacloud.com