Energy-efficient VM opening algorithms for real-time workflows in heterogeneous clouds - Long, Saiqin; Dai, Xin; Pei, Tingrui; Cao, Jiasheng; Sekiya, Hiroo; Choi, Young June (Neurocomputing, 2022)
Minimizing energy consumption is a critical challenge for real-time workflows, particularly in heterogeneous cloud computing systems. State-of-the-art algorithms aim to minimize the energy consumed for processing such applications by choosing virtual machines (VMs) to shut down from all opened VMs (i.e., VM merging). However, such VM merging through an “on-to-close” approach usually incurs high computational complexity. This paper proposes an energy-efficient VM opening (EEVO) algorithm that is capable of choosing VMs to turn on from all closed VMs while satisfying the real-time constraint of applications. Considering that there are slacks that can be eliminated or reduced between adjacently scheduled tasks after using the EEVO algorithm, a dynamic scaling down EEVO algorithm (DEEVO) is further proposed. DEEVO is implemented by scaling down the frequency of VMs executing each task based on the dynamic voltage and frequency scaling (DVFS) technique. Experimental results demonstrate that, with the above-mentioned improvements, DEEVO achieves lower energy consumption for real-time workflows than state-of-the-art algorithms do. In addition, DEEVO outperforms state-of-the-art algorithms in the computational efficiency of accomplishing task scheduling.
Keywords: Dynamic voltage and frequency scaling; Energy efficient; Energy-consumption; Real- time; Real-time workflow; Scaling down; State-of-the-art algorithms; Virtual machine opening; Work-flows; Dynamic voltage and frequency scaling (DVFS); Energy consumption; Real-time workflows; VM opening