An energy-efficient adaptive resource provision framework for cloud platforms

In cloud computing, resource provision service plays an important role for operating large-scale datacentres. Conventional resource provision policies or services mainly concentrate on optimising costs and application execution performance. In this paper, we present an integrated and adaptive resource provision framework, which is based on our previous work on performance monitor in cloud environments. In the proposed framework, several novel mechanisms are implemented, aiming at improving the energy-efficiency as well as the execution performance for cloud systems. Extensive experiments are conducted to evaluate the performance of the proposed framework in terms of different metrics. The experimental results show that the proposed framework can significantly improve the energy-efficiency metric, especially when a cloud system is in presence of intensive hybrid workloads.

[1]  Laurent Lefèvre,et al.  Designing and evaluating an energy efficient Cloud , 2010, The Journal of Supercomputing.

[2]  Greg Goth.  Data Center Operators Face Energy Irony , 2010, IEEE Internet Computing.

[3]  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..

[4]  Waheed Iqbal,et al.  Adaptive resource provisioning for read intensive multi-tier applications in the cloud , 2011, Future Gener. Comput. Syst..

[5]  Sunilkumar S. Manvi,et al.  Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey , 2014, J. Netw. Comput. Appl..

[6]  José Luis Vázquez-Poletti,et al.  Provisioning data analytic workloads in a cloud , 2013, Future Gener. Comput. Syst..

[7]  Josef Spillner,et al.  Creating optimal cloud storage systems , 2013, Future Gener. Comput. Syst..

[8]  Gianluigi Zanetti,et al.  Suspending, migrating and resuming HPC virtual clusters , 2010, Future Gener. Comput. Syst..

[9]  Erich Schikuta,et al.  Toward an economic and energy‐aware cloud cost model , 2013, Concurr. Comput. Pract. Exp..

[10]  Peng Xiao,et al.  Multi-scheme co-scheduling framework for high-performance real-time applications in heterogeneous grids , 2014, Int. J. Comput. Sci. Eng..

[11]  Rajkumar Buyya,et al.  Future Generation Computer Systems Deadline-driven Provisioning of Resources for Scientific Applications in Hybrid Clouds with Aneka , 2022 .

[12]  Tharam S. Dillon,et al.  A framework for SLA management in cloud computing for informed decision making , 2012, Cluster Computing.

[13]  Kun Wang,et al.  Self-adaptive provisioning of virtualized resources in cloud computing , 2011, SIGMETRICS '11.

[14]  Sujata Banerjee,et al.  On energy efficiency for enterprise and data center networks , 2011, IEEE Communications Magazine.

[15]  Ning Han,et al.  A novel power-conscious scheduling algorithm for data-intensive precedence-constrained applications in cloud environments , 2014, Int. J. High Perform. Comput. Netw..

[16]  Jin-Soo Kim,et al.  Cost optimized provisioning of elastic resources for application workflows , 2011, Future Gener. Comput. Syst..

[17]  Douglas C. Schmidt,et al.  Model-driven auto-scaling of green cloud computing infrastructure , 2012, Future Gener. Comput. Syst..

[18]  Mithuna Thottethodi,et al.  Dynamic server provisioning to minimize cost in an IaaS cloud , 2011, SIGMETRICS.

[19]  Sherif Sakr,et al.  Cloud-hosted databases: technologies, challenges and opportunities , 2014, Cluster Computing.

[20]  Schahram Dustdar,et al.  Cloud resource provisioning and SLA enforcement via LoM2HiS framework , 2013, Concurr. Comput. Pract. Exp..

[21]  Jun Wang,et al.  A survey on energy-efficient data management , 2011, SGMD.

[22]  Antonio Corradi,et al.  The management of cloud systems , 2014, Future Gener. Comput. Syst..

[23]  Qian Zhu,et al.  Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments , 2012, IEEE Trans. Serv. Comput..

[24]  Rajkumar Buyya,et al.  Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints , 2013, IEEE Transactions on Parallel and Distributed Systems.

[25]  Peng Xiao,et al.  Configurable performance analysis and evaluation framework for cloud systems , 2013, Int. J. Inf. Commun. Technol..

[26]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[27]  Andreas Berl,et al.  An energy consumption model for virtualized office environments , 2011, Future Gener. Comput. Syst..