Modelling the Live Migration Time of Virtual Machines

Dynamic server consolidation in data centres enables the efficient usage of resources, because it aims to minimise the underutilisation or overloading of physical servers, both of which produce a disproportional amount of energy consumption. Server consolidation takes place by migrating virtual machines from one server to another while the virtual machines are still executing. However, live migration comes with corresponding costs in terms of execution latency and additional resource and power consumption. Whether or not these costs are significant depends on how long a migration lasts. In this paper we propose models to estimate the time it takes to live migrate virtual machines at runtime. Our models are built using simple and multiple linear regressions. The paper reveals useful insights into the most important parameters which are strongly correlated with the migration time. These are: Instructions retired, last level cache line misses, and dirtying memory pages.

[1]  Waltenegus Dargie Analysis of the Power Consumption of a Multimedia Server under Different DVFS Policies , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[2]  A. Jaleel Memory Characterization of Workloads Using Instrumentation-Driven Simulation A Pin-based Memory Characterization of the SPEC CPU 2000 and SPEC CPU 2006 Benchmark Suites , 2022 .

[3]  Alexander Schill,et al.  Investigation into the energy cost of live migration of virtual machines , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[4]  Anja Strunk,et al.  A Lightweight Model for Estimating Energy Cost of Live Migration of Virtual Machines , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[5]  Robert Kabacoff,et al.  R in Action: Data Analysis and Graphics with R , 2015 .

[6]  Somayeh Malakuti,et al.  Mutual Influence of Application- and Platform-Level Adaptations on Energy-Efficient Computing , 2015, 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[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]  Christian Poellabauer,et al.  Feedback-based dynamic voltage and frequency scaling for memory-bound real-time applications , 2005, 11th IEEE Real Time and Embedded Technology and Applications Symposium.

[9]  Anja Strunk Costs of Virtual Machine Live Migration: A Survey , 2012, 2012 IEEE Eighth World Congress on Services.

[10]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[11]  Yutaka Ishikawa,et al.  An Efficient Process Live Migration Mechanism for Load Balanced Distributed Virtual Environments , 2010, 2010 IEEE International Conference on Cluster Computing.

[12]  J. W. Johnson A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression , 2000, Multivariate behavioral research.

[13]  SPEC CPU 2006 Benchmark Descriptions , 2006 .

[14]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[15]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[16]  Gautam Kumar,et al.  CosMig: Modeling the Impact of Reconfiguration in a Cloud , 2011, 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems.

[17]  Andy Hopper,et al.  Predicting the Performance of Virtual Machine Migration , 2010, 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[18]  Michael I. Baron,et al.  Probability and Statistics for Computer Scientists , 2013 .

[19]  Alexander Schill,et al.  Analysing the Migration Time of Live Migration of Multiple Virtual Machines , 2014, CLOSER.

[20]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

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

[22]  Rajkumar Buyya,et al.  Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation , 2009, CloudCom.

[23]  Christina Delimitrou,et al.  Quasar: resource-efficient and QoS-aware cluster management , 2014, ASPLOS.

[24]  Umesh Deshpande,et al.  Live gang migration of virtual machines , 2011, HPDC '11.

[25]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[26]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

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

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

[29]  Hai Jin,et al.  Performance and energy modeling for live migration of virtual machines , 2011, Cluster Computing.