Enhancing energy efficiency in resource allocation for real-time cloud services

Improving energy efficiency is a multidimensional challenge regarding cloud computing environments management, which can directly reduce the operating costs and carbon dioxide emissions, while increasing the system reliability. An energy-efficient resource allocation approach for real-time cloud services is proposed in this paper. Several policies for provisioning of virtual machines (VMs) and hosts for the purpose of minimizing energy consumption and deadline miss rate are presented here. Resource allocation is optimized in order to increase the acceptance rate of real-time tasks through VM scaling and migration. This proposed algorithm significantly reduces energy consumption, and guarantees the timing constraints of tasks in an effective manner.

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

[2]  Albert Y. Zomaya,et al.  A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems , 2010, Adv. Comput..

[3]  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..

[4]  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..

[5]  Rajkumar Buyya,et al.  Power‐aware provisioning of virtual machines for real‐time Cloud services , 2011, Concurr. Comput. Pract. Exp..

[6]  Rajkumar Buyya,et al.  Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

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

[8]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[9]  Saurabh Kumar,et al.  Energy Efficient Utilization of Resources in Cloud Computing Systems , 2016 .

[10]  J. Koomey Worldwide electricity used in data centers , 2008 .

[11]  Rajkumar Buyya,et al.  Energy-aware simulation with DVFS , 2013, Simul. Model. Pract. Theory.

[12]  Kenli Li,et al.  Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters , 2012, J. Parallel Distributed Comput..

[13]  Gang Quan,et al.  On-Line Scheduling of Real-Time Services for Cloud Computing , 2010, 2010 6th World Congress on Services.