IoP System Dependability Evaluation Method Based on AADL

Shi, Xiaodong; Mian, Zhibao; Gao, Yaoqing

Abstract

The Internet of People(IoP)is characterized by the complex architecture and massive changing data, which adds to the difficulty of the analysis on IoP-based system dependability.Currently, there is still no robust dependability modelling and analysis method for IoP systems. This paper proposes an Architecture Analysis and Design Language (AADL)-based dependability evaluation method for IoP systems. By using AADL and its annex language, the dependability of IoP systems is modeled to support the qualitative analysis on the causes of system failures and risks. Furthermore, by combining the Ocarina model transformation technology, a quantitative evaluation algorithm based on the Continuous-Time Markov Chain(CTMC)is proposed. The algorithm transforms the AADL dependability model to the CTMC model, so that the dynamic and real-time attributes of IoP systems can be evaluated quantitatively. On this basis, a general IoP system model is designed to demonstrate the feasibility of the proposed method. The experimental results show that the proposed method can be used to model the IoP systems, and perform dependability analysis automatically and accurately, displaying a high application value.

Citation

Shi, X., Mian, Z., & Gao, Y. (2022). IoP System Dependability Evaluation Method Based on AADL. Jisuanji Gongcheng/Computer Engineering, 48(1), 204-213. https://doi.org/10.19678/j.issn.1000-3428.0059738

• Journal Article Type: Article
• Acceptance Date: Jan 3, 2022
• Online Publication Date: Jan 3, 2022
• Publication Date: Jan 10, 2022
• Deposit Date: Jul 12, 2022
• Publisher: Shanghai Jisuanji Xuehui/Shanghai Computer Society
• Peer Reviewed: Peer Reviewed
• Volume: 48
• Issue: 1
• Pages: 204-213
• DOI: https://doi.org/10.19678/j.issn.1000-3428.0059738
• Keywords: Internet of people; Architecture Analysis and Design Language; Reliability modeling and analysis; continuous-time Markov chain; Ocarina model conversion
• Public URL: https://hull-repository.worktribe.com/output/4028749