Dynamic virtual machine placement considering CPU and memory resource requirements

Mosa, Abdelkhalik and Sakellariou, Rizos; (2019) Dynamic virtual machine placement considering CPU and memory resource requirements. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, Piscataway, N.J., pp. 196-198. ISBN 9781728127064 (https://doi.org/10.1109/CLOUD.2019.00042)

[thumbnail of Mosa-Sakellariou-ICCC-2019-Dynamic-virtual-machine-placement-considering-CPU-and-memory]
Preview
 Text. Filename: Mosa_Sakellariou_ICCC_2019_Dynamic_virtual_machine_placement_considering_CPU_and_memory.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (633kB)| Preview

Abstract

In cloud data centers, cloud providers can offer computing infrastructure as a service in the form of virtual machines (VMs). With the help of virtualization technology, cloud data centers can consolidate VMs on physical machines to minimize costs. VM placement is the process of assigning VMs to the appropriate physical machines. An efficient VM placement solution will result in better VM consolidation ratios which ensures better resource utilization and hence more energy savings. The VM placement process consists of both the initial as well as the dynamic placement of VMs. In this paper, we are experimenting with a dynamic VM placement solution that considers different resource types (namely, CPU and memory). The proposed solution makes use of a genetic algorithm for the dynamic reallocation of the VMs based on the actual demand of the individual VMs aiming to minimize under-utilization and over-utilization scenarios in the cloud data center. Empirical evaluation using CloudSim highlights the importance of considering multiple resource types. In addition, it demonstrates that the genetic algorithm outperforms the well-known best-fit decreasing algorithm for dynamic VM placement.

CORE (COnnecting REpositories)