Efficient workload management in geographically distributed data centers leveraging autoregressive models
暂无分享,去创建一个
The opportunity of using Cloud resources on a pay-as-you-go basis and the availability of powerful data centers and high bandwidth connections are speeding up the success and popularity of Cloud systems, which is making on-demand computing a common practice for enterprises and scientific communities. The reasons for this success include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Nevertheless, the expansion of large data centers is resulting in a huge rise of electrical power consumed by hardware facilities and cooling systems. The geographical distribution of data centers is becoming an opportunity: the variability of electricity prices, environmental conditions and client requests, both from site to site and with time, makes it possible to intelligently and dynamically (re)distribute the computational...
[1] Michela Meo,et al. Hierarchical Approach for Green Workload Management in Distributed Data Centers , 2014, Euro-Par Workshops.
[2] Eugenio Cesario,et al. Predictive Models for Energy-Efficient Clouds: An Analysis on Real-Life and Synthetic Data , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.