A Mirror Environment to Produce Artificial Intelligence Training Data

Abstract

With the increasing maturity of artificial intelligence (AI) technology, business automation technology has also become a trend. Particularly, network operation and maintenance (O&M) is expected to soon become automated and more efficient. However, the automation of O&M is hindered by the lack of network failure data and the cost of collecting data. We thus propose an approach to build a low-cost environment that can produce the same data as the actual production environment and use tools such as chaos engineering to generate training models for fault data. This paper attempts to build the underlying physical network layer using a low-cost single-board computer Raspberry Pi instead of an expensive PC server, while keeping the virtual network layer the same and performing fault simulation, data collection, and AI model training on the constructed virtual network layer. A comparison of the accuracy of the trained AI models verifies the feasibility of replacing the traditional PC server with an inexpensive Raspberry Pi device while keeping the structure and services of the virtual network layer unchanged. Also, a brief comparison with existing techniques is discussed. Our proposed approach solves the problem of insufficient data for AI training while reducing cost and risk.

Keywords

• Single-board computers
• Raspberry Pi
• low cost
• network fault
• chaos engineering
• insufficient data