Energy-aware scheduling in edge computing with a clustering method

• Published in: Future generation computer systems Vol. 117; pp. 259 - 272
• Main Authors: Hao, Yongsheng, Cao, Jie, Wang, Qi, Du, Jinglin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-04-2021
• Subjects: Cluster; Edge computing; Energy consumption; Energy-aware; Energy consumption; Cluster; Energy-aware; Edge computing
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2020.11.029

With the development of Cloud and 5G technology, edge devices have been widely used in various areas. However, the limited battery energy and processing ability of edge devices hinder the usage scope of those devices. Prior studies have typically managed to immigrate virtual machines (VMs) or offload tasks to reduce energy consumption and shorten execution time. In our work, we consider devices that can obtain energy from a green energy source (such as wind energy, or solar energy). First, we use a clustering method to divide nodes into some clusters, each with some edge nodes to ensure the clusters have a minimum distance (defined by energy transferring attenuation ratios between nodes) between nodes in the cluster; the cluster center is a node with a VM. Then, based on the clustering method, a scheduling heuristic is proposed to transfer energy, immigrate VM, and allocate tasks. The simulation result shows that our proposed method reduces both the total energy consumption and the energy consumption from outside of the system (ECFO).
•An edge computing framework supporting green energy is presented.•The stable performance of network and energy transfer routes are analyzed.•A cluster method is used to immigrate VMs.•A heuristic scheduling method is used to schedule tasks and energy.