A novel green software evaluation model for cloud robotics. (October 2017)

• Record Type: Journal Article
• Authors: Hou, Gang; Zhou, Kuanjiu; Qiu, Tie; Cao, Xun; Li, Mingchu; Wang, Jie
• Is Part Of: Computers & electrical engineering. Volume 63(2017)
• Page Start: 139
• Page End: 156
• Publication Date: 2017-10
• Subjects: Cloud robotics -- Energy consumption -- Green software modeling method -- State transition matrix -- Bounded model checking; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.021
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A novel green software evaluation model named ETSTM is proposed. An energy consumption function based on software characteristic is provided. The multi-scale green evaluation algorithms of software are put forward. Graphical abstract: Abstract: The energy consumption of cloud robotics is an important design factor to be considered early in system development. Software as the main enabler of cloud robots, its energy consumption will directly influence the energy consumption level of the entire system. In this paper, we propose a green software model for cloud robotics based on energy consumption time state transition matrix (ETSTM), which can effectively integrate the logic functions, energy consumption, and execution time of software into a single model. To improve the practicability of ETSTM, we provide a software energy consumption function based on software characteristic fitting by a backpropagation (BP) neural network, which can be used to predict software energy consumption. Furthermore, we provide two types of energy consumption analysis algorithms based on explicit execution path search and bounded model checking (BMC) for ETSTM. The experiment results show that the proposed method can achieve good performance.