Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems. (12th January 2021)

• Record Type: Journal Article
• Authors: Cao, Kun; Wei, Tongquan; Chen, Mingsong; Li, Keqin; Weng, Jian; Tan, Wuzheng
• Other Names: Zhou Junlong guestEditor.; Kritikakou Angeliki guestEditor.; Zhu Dakai guestEditor.; Lastra Jose L. Martinez guestEditor.; Hu Shiyan guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 11(2021)
• Page Start: 2225
• Page End: 2237
• Publication Date: 2021-01-12
• Subjects: cyber‐physical systems -- edge‐cloud computing -- energy -- reliability -- service latency minimization; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2942
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: In recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.