Energy Efficiency Policies for Smart Digital Cloud Environment based on Heuristics Algorithms

The Cloud computing model is based on the use of virtual resources and their placement on physical servers hosted in the different data centers. Those data centers are known to be big energy consumers. The allocation of virtual machines within servers has a paramount role in optimizing energy consumption of the underlying infrastructure in order to satisfy the environmental and economic constraints. Since then, various hardware and software solutions have emerged. Among these strategies, we highlight the optimization of virtual machine scheduling in order to improve the quality of service and the energy efficiency. Through this paper, we propose firstly, to study energy consumption in the Cloud environment based on the GreenCloud simulator. Secondly, we define a scheduling solution aimed at reducing energy consumption via a better resource allocation strategy by privileging data center powered by clean energy. The main contributions of this paper are the use of the Taguchi concept to evaluate the Cloud model and the introduction of scheduling policy based on the simulated annealing algorithm.

[1]  Stefano Avallone,et al.  A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers , 2018, Comput. Networks.

[2]  Dzmitry Kliazovich,et al.  GreenCloud: a packet-level simulator of energy-aware cloud computing data centers , 2010, The Journal of Supercomputing.

[3]  JinHai,et al.  Optimizing the live migration of virtual machine by CPU scheduling , 2011 .

[4]  Zhigang Hu,et al.  A novel virtual machine deployment algorithm with energy efficiency in cloud computing , 2015 .

[5]  Wei Wang,et al.  Study of a Virtual Machine Migration Method , 2013, 2013 International Conference on Advanced Cloud and Big Data.

[6]  Dzmitry Kliazovich,et al.  Accounting for load variation in energy-efficient data centers , 2013, 2013 IEEE International Conference on Communications (ICC).

[7]  Christoph Aschberger,et al.  Energy Efficiency in Cloud Computing , 2013 .

[8]  Stefano Avallone,et al.  A Simulated Annealing Based Approach for Power Efficient Virtual Machines Consolidation , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[9]  Dzmitry Kliazovich,et al.  A Holistic Model for Resource Representation in Virtualized Cloud Computing Data Centers , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[10]  J. Douglas Barrett,et al.  Taguchi's Quality Engineering Handbook , 2007, Technometrics.

[11]  Hai Jin,et al.  Optimizing the live migration of virtual machine by CPU scheduling , 2011, J. Netw. Comput. Appl..

[12]  Claudia Canali,et al.  A Computation- and Network-Aware Energy Optimization Model for Virtual Machines Allocation , 2017, CLOSER.

[13]  Marta Chinnici,et al.  An Example of Methodology to Assess Energy Efficiency Improvements in Datacenters , 2013, 2013 International Conference on Cloud and Green Computing.

[14]  Kenli Li,et al.  A Multi-objective Virtual Machine Migration Policy in Cloud Systems , 2014, Comput. J..

[15]  Toby Velte,et al.  Cloud Computing, A Practical Approach , 2009 .

[16]  Barrie Sosinsky,et al.  Cloud Computing Bible , 2010 .

[17]  Dzmitry Kliazovich,et al.  DENS: data center energy-efficient network-aware scheduling , 2010, Cluster Computing.

[18]  Ayman I. Kayssi,et al.  CloudESE: Energy efficiency model for cloud computing environments , 2011, 2011 International Conference on Energy Aware Computing.

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