Bandwidth Management VMs Live Migration in Wireless Fog Computing for 5G Networks

Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data-centers, so to lead to reduced energy consumption. Here, we analytically characterize, prototype in software and test an optimal bandwidth manager for live migration of VMs in wireless channel. In this paper we present the optimal tunable-complexity bandwidth manager (TCBM) for the QoS live migration of VMs under a wireless channel from smartphone to access point. The goal is the minimization of the migration-induced communication energy under service level agreement (SLA)-induced hard constrains on the total migration time, downtime and overall available bandwidth.

[1]  Byung-Gon Chun,et al.  CloneCloud: elastic execution between mobile device and cloud , 2011, EuroSys '11.

[2]  Victor C. M. Leung,et al.  EMC: Emotion-aware Mobile Cloud Computing , 2015 .

[3]  Enzo Baccarelli,et al.  Resource-Management for Vehicular Real-Time Application under Hard Reliability Constraints , 2014, 2014 IEEE/ACM 18th International Symposium on Distributed Simulation and Real Time Applications.

[4]  Enzo Baccarelli,et al.  Energy-efficient adaptive networked datacenters for the QoS support of real-time applications , 2014, The Journal of Supercomputing.

[5]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[6]  Enzo Baccarelli,et al.  Stochastic traffic engineering for real-time applications over wireless networks , 2012, J. Netw. Comput. Appl..

[7]  Athanasios V. Vasilakos,et al.  Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions , 2014, Proceedings of the IEEE.

[8]  Ming Zhao,et al.  Performance Modeling of Virtual Machine Live Migration , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[9]  David Chisnall,et al.  The Definitive Guide to the Xen Hypervisor , 2007 .

[10]  Laura Vasiliu,et al.  CloneCloud: Elastic Execution between Mobile Device and Cloud , 2012 .

[11]  Arun Venkataramani,et al.  Black-box and Gray-box Strategies for Virtual Machine Migration , 2007, NSDI.

[12]  Jörg Widmer,et al.  Survey on Energy Consumption Entities on the Smartphone Platform , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[13]  Markus Schüring Mobile cloud computing - open issues and solutions , 2011 .

[14]  Enzo Baccarelli,et al.  Minimum-energy bandwidth management for QoS live migration of virtual machines , 2015, Comput. Networks.

[15]  Enzo Baccarelli,et al.  Hard and soft optimal resource allocation for primary and secondary users in infrastructure Vehicular Networks , 2015, 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC).

[16]  Burak Kantarci,et al.  Communication Infrastructures for Cloud Computing , 2013 .

[17]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[18]  Geoffrey C. Fox,et al.  Distributed and Cloud Computing: From Parallel Processing to the Internet of Things , 2011 .

[19]  Feng Qian,et al.  A close examination of performance and power characteristics of 4G LTE networks , 2012, MobiSys '12.

[20]  Henri E. Bal,et al.  Cuckoo: A Computation Offloading Framework for Smartphones , 2010, MobiCASE.

[21]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[22]  Enzo Baccarelli,et al.  Broadband Wireless Access Networks: A Roadmap on Emerging Trends and Standards , 2005 .

[23]  Victor C. M. Leung,et al.  EMC: Emotion-aware mobile cloud computing in 5G , 2015, IEEE Network.

[24]  David S. Rosenblum,et al.  VOLARE: context-aware adaptive cloud service discovery for mobile systems , 2010, ARM '10.