Oriented-SLA and Energy-Efficient Virtual Machine Management Strategy of Cloud Data Centers

Cloud computing has revolutionized the information and communication technology industry by enabling on-demand provisioning of elastic computing resources on a pay-asyou-go basis. However, due to the large-scale popularization and application, cloud data centers consume enormous amounts of electrical energy, which results in high operating costs and carbon dioxide emissions. In this paper, twenty-two algorithms are simulated for testing the trade-off between the applications performance and energy consumption during virtual machine management of cloud data centers in CloudSim simulation toolkit. The goal of these algorithms is to reduce energy consumption under ensuring the performace of cloud applications. A large number of simulation experimental results prove the performance of these twenty-two algorithms respectively.

[1]  Bu-Sung Lee,et al.  Optimal virtual machine placement across multiple cloud providers , 2009, 2009 IEEE Asia-Pacific Services Computing Conference (APSCC).

[2]  Luis Rodero-Merino,et al.  A break in the clouds: towards a cloud definition , 2008, CCRV.

[3]  Yefu Wang,et al.  Coordinating Power Control and Performance Management for Virtualized Server Clusters , 2011, IEEE Transactions on Parallel and Distributed Systems.

[4]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[5]  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 .

[6]  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..

[7]  Jean-Marc Menaud,et al.  Performance and Power Management for Cloud Infrastructures , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[8]  Calton Pu,et al.  Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures , 2010, 2010 IEEE 30th International Conference on Distributed Computing Systems.

[9]  G. Magklis,et al.  Dynamic Frequency and Voltage Scaling for a Multiple-Clock-Domain Microprocessor , 2003, IEEE Micro.

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

[11]  Li Qiang,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .

[12]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .