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Information 2018, 9(1), 4;

An RTT-Aware Virtual Machine Placement Method

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
School of Computer and Information, Hefei University of Technology, Hefei 230000, China
Author to whom correspondence should be addressed.
Received: 29 November 2017 / Revised: 26 December 2017 / Accepted: 26 December 2017 / Published: 29 December 2017
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Virtualization is a key technology for mobile cloud computing (MCC) and the virtual machine (VM) is a core component of virtualization. VM provides a relatively independent running environment for different applications. Therefore, the VM placement problem focuses on how to place VMs on optimal physical machines, which ensures efficient use of resources and the quality of service, etc. Most previous work focuses on energy consumption, network traffic between VMs and so on and rarely consider the delay for end users’ requests. In contrast, the latency between requests and VMs is considered in this paper for the scenario of optimal VM placement in MCC. In order to minimize average RTT for all requests, the round-trip time (RTT) is first used as the metric for the latency of requests. Based on our proposed RTT metric, an RTT-Aware VM placement algorithm is then proposed to minimize the average RTT. Furthermore, the case in which one of the core switches does not work is considered. A VM rescheduling algorithm is proposed to keep the average RTT lower and reduce the fluctuation of the average RTT. Finally, in the simulation study, our algorithm shows its advantage over existing methods, including random placement, the traffic-aware VM placement algorithm and the remaining utilization-aware algorithm. View Full-Text
Keywords: virtual machine placement; round-trip time; cloud computing; mobile cloud computing virtual machine placement; round-trip time; cloud computing; mobile cloud computing

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Quan, L.; Wang, Z.; Ren, F. An RTT-Aware Virtual Machine Placement Method. Information 2018, 9, 4.

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