.png){kind=icon}
.png){kind=icon}
Return
A high-efficiency learning model for virtual machine placement in mobile edge computing
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-022-03550-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Mobile edge computing requires more and more high-performance servers, resulting in increasing energy consumption. As a well-established way to reduce energy consumption, virtual machine placement can be utilized to optimize the cost of wide-deployment of servers. However, traditional strategies tend to focus on single indicators, there are few existing research taking time delay limitation into account while solving energy consumption problems. In this paper, we propose a brand new method to settle the problems listed above, which is able to reduce the placement time of virtual machine and energy consumption. First, considering the excellent performance of bat swarm algorithm in NP-hard problem, we introduced the second order oscillation factor to avoid premature convergence, and combined the order exchange and migration local search technology. we proposed the OEMBA algorithm, which integrates underutilized servers to save energy. Subsequently, an improved Long Short-Term Memory model is utilized to fasten the placement of virtual machines and reduce latency based on historical data. Our results indicate that the improved learning model can save energy consumption and reduce placement latency.
Keywords:
Mobile edge computing
Machine learning
Virtual machine placement
Bat Algorithm
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K