An Energy-aware resource management scheme of Data Centres for eco-friendly cloud computing

Cloud computing, NP-Hard problem, Simulated Annealing, Data centres, Virtual Machine. Cloud computing is one of the most popular technologies at recent times that delivers ondemand applications and resources over the Internet. The main hub of the cloud computing is numerous data centres, from where all of the services are disseminated towards the end users. Although cloud computing is being appeared as a big business in IT industry, consumption of too much energy in cloud data centres has created new concern, especially in terms of increasing energy-related costs and carbon dioxide emission rate. Therefore, cloud resources, such as CPU, memory, networks, need to be managed energy-efficiently to reduce operational costs as well as the negative consequences of cloud computing on our natural environment. In this paper, we explore a VM (Virtual Machine) consolidation model that focuses on minimizing power consumption and resource wastage. As VM consolidation is NP-Hard problem, we also suggest efficient resource allocation scheme using meta-heuristic algorithm, such as simulated annealing (SA), which try to optimize VM consolidation and ensure proper utilization of resources.

[1]  Paolo Toth,et al.  Lower bounds and algorithms for the 2-dimensional vector packing problem , 2001, Discret. Appl. Math..

[2]  Paolo Toth,et al.  A Set-Covering-Based Heuristic Approach for Bin-Packing Problems , 2006, INFORMS J. Comput..

[3]  Christoforos E. Kozyrakis,et al.  JouleSort: a balanced energy-efficiency benchmark , 2007, SIGMOD '07.

[4]  David Levine,et al.  Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning , 2007, NIPS.

[5]  Rajarshi Das,et al.  Autonomic multi-agent management of power and performance in data centers , 2008, AAMAS.

[6]  Liang Liu,et al.  GreenCloud: a new architecture for green data center , 2009, ICAC-INDST '09.

[7]  Yasushi Inoguchi,et al.  Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[8]  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.

[9]  Erol Gelenbe,et al.  Energy-Efficient Cloud Computing , 2010, Comput. J..

[10]  Albert Y. Zomaya,et al.  Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems , 2011 .

[11]  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..

[12]  Rajkumar Buyya,et al.  Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers , 2011, J. Parallel Distributed Comput..

[13]  Rajkumar Buyya,et al.  Green Cloud Framework for Improving Carbon Efficiency of Clouds , 2011, Euro-Par.

[14]  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..

[15]  Yu Jiong,et al.  Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing , 2012, 2012 Seventh ChinaGrid Annual Conference.

[16]  Rajkumar Buyya,et al.  Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic , 2014, Euro-Par.

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

[18]  Sudip Misra,et al.  Cloud Computing Applications for Smart Grid: A Survey , 2015, IEEE Transactions on Parallel and Distributed Systems.