Balancing the Use of Batteries and Opportunistic Scheduling Policies for Maximizing Renewable Energy Consumption in a Cloud Data Center

The fast growth of cloud computing considerably increases the energy consumption of cloud infrastructures, especially, data centers. To reduce brown energy consumption and carbon footprint, renewable energy such as solar/wind energy is considered recently to supply new green data centers. As renewable energy is intermittent and fluctuates from time to time, this paper considers two fundamental approaches for improving the usage of renewable energy in a small/medium-sized data center. One approach is based on opportunistic scheduling: more jobs are performed when renewable energy is available. The other approach relies on Energy Storage Devices (ESDs), which store renewable energy surplus at first and then, provide energy to the data center when renewable energy becomes unavailable. In this paper, we explore these two means to maximize the utilization of on-site renewable energy for small data centers. By using real-world job workload and solar energy traces, our experimental results show the energy consumption with varying battery size and solar panel dimensions for opportunistic scheduling or ESD-only solution. The results also demonstrate that opportunistic scheduling can reduce the demand for ESD capacity. Finally, we find an intermediate solution mixing both approaches in order to achieve a balance in all aspects, implying minimizing the renewable energy losses. It also saves brown energy consumption by up to 33% compared to ESD-only solution.

[1]  Djamal Zeghlache,et al.  Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[2]  Emmanuel Jeannot,et al.  Adding Virtualization Capabilities to the Grid'5000 Testbed , 2012, CLOSER.

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

[4]  Laurent Lefèvre,et al.  A survey on techniques for improving the energy efficiency of large-scale distributed systems , 2014, ACM Comput. Surv..

[5]  Gregor von Laszewski,et al.  Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[6]  Yanpei Chen,et al.  Integrating Renewable Energy Using Data Analytics Systems: Challenges and Opportunities , 2011, IEEE Data Eng. Bull..

[7]  Anand Sivasubramaniam,et al.  Energy storage in datacenters: what, where, and how much? , 2012, SIGMETRICS '12.

[8]  Xiangliang Zhang,et al.  Virtual machine migration in an over-committed cloud , 2012, 2012 IEEE Network Operations and Management Symposium.

[9]  Thu D. Nguyen,et al.  Parasol and GreenSwitch: managing datacenters powered by renewable energy , 2013, ASPLOS '13.

[10]  M. Yue A simple proof of the inequality FFD (L) ≤ 11/9 OPT (L) + 1, ∀L for the FFD bin-packing algorithm , 1991 .

[11]  Mor Harchol-Balter,et al.  Optimal power allocation in server farms , 2009, SIGMETRICS '09.

[12]  Catherine Rosenberg,et al.  Toward a Realistic Performance Analysis of Storage Systems in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[13]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[14]  Thomas Ledoux,et al.  Towards energy-proportional clouds partially powered by renewable energy , 2016, Computing.

[15]  M. Savoie,et al.  Converged Optical Network Infrastructures in Support of Future Internet and Grid Services Using IaaS to Reduce GHG Emissions , 2009, Journal of Lightwave Technology.

[16]  Mohsen Guizani,et al.  Efficient datacenter resource utilization through cloud resource overcommitment , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[17]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[18]  Jean-Marc Menaud,et al.  Opportunistic Scheduling in Clouds Partially Powered by Green Energy , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[19]  Rajkumar Buyya,et al.  Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges , 2010, PDPTA.

[20]  Mateo Valero,et al.  Understanding the future of energy-performance trade-off via DVFS in HPC environments , 2012, J. Parallel Distributed Comput..

[21]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.