An Experimental Comparison of Algorithms for Virtual Machine Placement Considering Many Objectives

Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of five different online deterministic heuristics against an offline memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results prove that an offline memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement reconfigurations.

[1]  Martin Bichler,et al.  A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers , 2010, IEEE Transactions on Services Computing.

[2]  Deng Pan,et al.  Efficient VM placement with multiple deterministic and stochastic resources in data centers , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[3]  Benjamín Barán,et al.  A Many-objective Optimization Framework for Virtualized Datacenters , 2015, CLOSER.

[4]  Benjamín Barán,et al.  A Virtual Machine Placement Taxonomy , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

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

[6]  Benjamín Barán,et al.  Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[7]  Priyanka Sharma,et al.  Survey of virtual machine placement in federated clouds , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[8]  S. K. Nandy,et al.  Virtual Machine Placement Optimization Supporting Performance SLAs , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[9]  César A. F. De Rose,et al.  Maximum Migration Time Guarantees in Dynamic Server Consolidation for Virtualized Data Centers , 2011, Euro-Par.

[10]  Xiuqi Li,et al.  Virtual machine consolidated placement based on multi-objective biogeography-based optimization , 2016, Future Gener. Comput. Syst..

[11]  John Herbert,et al.  Energy Efficient VM Placement Supported by Data Analytic Service , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[12]  Benjamín Barán,et al.  Many-objective virtual machine placement for dynamic environments , 2015, 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC).

[13]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

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

[15]  Samir Ben Salem,et al.  A high performances CMOS CCII and high frequency applications , 2006 .

[16]  Bu-Sung Lee,et al.  Power-Efficient Virtual Machine Placement and Migration in Data Centers , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[17]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[18]  Benjamín Barán,et al.  A Taxonomy on Dynamic Environments for Provider-Oriented Virtual Machine Placement , 2016, 2016 IEEE International Conference on Cloud Engineering (IC2E).