Energy-aware virtual machine selection method for cloud data center resource allocation

Saving energy is an important issue for cloud providers to reduce energy cost in a data center. With the increasing popularity of cloud computing, it is time to examine various energy reduction methods for which energy consumption could be reduced and lead us to green cloud computing. In this paper, our aim is to propose a virtual machine selection algorithm to improve the energy efficiency of a cloud data center. We are also presenting experimental results of the proposed algorithm in a cloud computing based simulation environment. The proposed algorithm dynamically took the virtual machines' allocation, deallocation, and reallocation action to the physical server. However, it depends on the load and heuristics based on the analysis placement of a virtual machine which is decided over time. From the results obtained from the simulation, we have found that our proposed virtual machine selection algorithm reduces the total energy consumption by 19% compared to the existing one. Therefore, the energy consumption cost of a cloud data center reduces and also lowers the carbon footprint. Simulation-based experimental results show that the proposed heuristics which are based on resource provisioning algorithms reduce the energy consumption of the cloud data center and decrease the virtual machine's migration rate.

[1]  Mohamed Othman,et al.  Energy efficient virtual machine provisioning in cloud data centers , 2014, 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT).

[2]  Karsten Schwan,et al.  VirtualPower: coordinated power management in virtualized enterprise systems , 2007, SOSP.

[3]  Marco Mellia,et al.  Energy saving and network performance: a trade-off approach , 2010, e-Energy.

[4]  Paul Barford,et al.  Generating representative Web workloads for network and server performance evaluation , 1998, SIGMETRICS '98/PERFORMANCE '98.

[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]  Derek McAuley,et al.  Energy is just another resource: energy accounting and energy pricing in the Nemesis OS , 2001, Proceedings Eighth Workshop on Hot Topics in Operating Systems.

[7]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[8]  Gargi Dasgupta,et al.  Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.

[9]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[10]  Anoop Gupta,et al.  Performance isolation: sharing and isolation in shared-memory multiprocessors , 1998, ASPLOS VIII.

[11]  Dror G. Feitelson,et al.  Workload Modeling for Performance Evaluation , 2002, Performance.

[12]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[13]  Enrique V. Carrera,et al.  Load balancing and unbalancing for power and performance in cluster-based systems , 2001 .

[14]  Mohamed Othman,et al.  Energy aware resource allocation of cloud data center: review and open issues , 2016, Cluster Computing.

[15]  Mohamed Othman,et al.  Optimized load balancing for efficient resource provisioning in the cloud , 2014, 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT).

[16]  Chia-Ming Wu,et al.  A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters , 2014, Future Gener. Comput. Syst..

[17]  Mohamed Othman,et al.  Brokering and Load-Balancing Mechanism in the Cloud – Revisited , 2014 .

[18]  Mohamed Othman,et al.  Cost-aware service brokering and performance sentient load balancing algorithms in the cloud , 2016, J. Netw. Comput. Appl..

[19]  Margo I. Seltzer,et al.  Isolation with Flexibility: A Resource Management Framework for Central Servers , 2000, USENIX Annual Technical Conference, General Track.

[20]  Liang Liu,et al.  Energy efficient scheduling of virtual machines in cloud with deadline constraint , 2015, Future Gener. Comput. Syst..

[21]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[22]  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..

[23]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[24]  Hui Li,et al.  Workload dynamics on clusters and grids , 2008, The Journal of Supercomputing.

[25]  Allan Borodin,et al.  On the power of randomization in on-line algorithms , 2005, Algorithmica.

[26]  Martin Bichler,et al.  More than bin packing: Dynamic resource allocation strategies in cloud data centers , 2015, Inf. Syst..

[27]  Mohamed Othman,et al.  EVALUATION OF CLOUD BROKERING ALGORITHMS IN CLOUD BASED DATA CENTER , 2015 .

[28]  Ibrahim Matta,et al.  BRITE: an approach to universal topology generation , 2001, MASCOTS 2001, Proceedings Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[29]  KyoungSoo Park,et al.  CoMon: a mostly-scalable monitoring system for PlanetLab , 2006, OPSR.

[30]  Allan Borodin,et al.  Online computation and competitive analysis , 1998 .

[31]  Mateusz Jarus,et al.  Performance bounded energy efficient virtual machine allocation in the global cloud , 2014, Sustain. Comput. Informatics Syst..

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

[33]  Fei Li,et al.  Empirical Study on the Evolution of PlanetLab , 2007, Sixth International Conference on Networking (ICN'07).

[34]  Mohamed Othman,et al.  Diverse approaches to cloud brokering: innovations and issues , 2017, Int. J. Commun. Networks Distributed Syst..

[35]  Minglu Li,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Computing Environments Based on Demand Forecast , 2012, GPC.