Abstract
Edge computing has been widely researched when 5G network and cloud platforms work together for people’s life. The limitation of energy provided by the battery of the edge device hinders its application. This paper focuses on task scheduling in edge computing combined with the Energy harvesting technology (EH) and Energy Internet (EI). The edge node collects green energy by EH. And nodes exchange energy by EI. Energy Internet obtains green energy from edge nodes. Compared to green energy, we call energy from the power grid (not from the energy of edge nodes by energy harvesting technology) brown energy. How to reduce brown energy consumption is one of the most important problem in our paper. Previous works have not examined the energy attenuation between nodes, neither have they studied the immigration routes of virtual machines (VMs). This paper analyzes VM scheduling and models the energy consumption of VM immigrations, offloading tasks, and green energy transfer in edge computing. The paper proposes a heuristic assumption that there is only one VM in the system, and then presents three heuristics for the system with multiple VMs. The simulation results show that the proposed - immigrated VMs with the minimum energy transferring attenuation ratio method (METAR) is effective in reducing brown energy and total energy consumption, and improving the utilization rate of green energy. Compared to the Energy-Efficiency problem solution (EE-PRO) and maximize task energy consumption scheduling (MTS), METAR average reduces by 28.23% and 49.50% in brown energy consumption. At the same time, METAR average decreases by 5.67% and 11.52% in execution time.
Highlights
With the rapid growth of smartphones, wireless technology [1], mobile devices, online applications [2], especially with the emergence of 5G network [3], edge computing is becoming increasingly important and popular [4] because it improves the quality of people’s lives in almost all aspects including work, society [5] and economy [6]
The work and main contributions are listed as follows: We combine Energy Internet (EI) with edge computing to enhance edge computing in terms of energy consumption; We model edge computing with EI technology with multiple virtual machines (VMs) and employ a method based on a global search to schedule tasks; Three heuristics are proposed to minimize the brown energy consumption based on three rules; Comparisons are made to evaluate the performance of the proposed methods and other methods
We evaluate the performance of energy consumption of VM immigration (ECVMI), energy lost (EL: for offloading tasks and green energy transferring), FIGURE 12
Summary
With the rapid growth of smartphones, wireless technology [1], mobile devices, online applications [2], especially with the emergence of 5G network [3], edge computing is becoming increasingly important and popular [4] because it improves the quality of people’s lives in almost all aspects including work, society [5] and economy [6]. To cope with the challenges of edge computing, tasks are offloaded from local nodes to nearby edge nodes [5] (or remote cloud) connected by cables and reticles to improve processing ability and battery working time [12]. Gateways, base stations, and other kinds of access points, the system improves the computational ability and prolongs the working time of a mobile edge node by offloading some tasks from a local edge node to other edge nodes with more energy and higher processing ability [13]. These metrics affect task processing time and the energy consumption of VM immigration and task processing. The second layer not supports communication (or improves processing ability) between edge nodes and transfers energy between them with EI technology [14, 15]. We can use ruler (1) and (2) alternately, called the method ALT
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