Joint Resource Scheduling and Peak Power Shaving for Cloud Data Centers with Distributed Uninterruptible Power Supply

The grid company enforces high penalties for the peak power demands of cloud data centers. These high penalties result in high electricity bill that can be avoided by relying on the servers' Uninterruptible Power Supply (UPS) as a source of energy during peak load periods. This paper proposes a management framework that exploits the distributed UPS batteries in order to minimize the total cluster's electricity bill. Our framework consists of: $i)$ a scheduler that accounts for both the amount of stored energy and the available resource slacks when making workload placement decision, and $ii)$ a power distributor that decides which UPS battery should store energy and by how much in order to increase the amount of energy that can be accessible for shaving the peak during high-demand periods. Several evaluations based on real Google traces show that our proposed framework achieves significant monetary savings.

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

[2]  Haipeng Luo,et al.  Adaptive Resource Provisioning for the Cloud Using Online Bin Packing , 2014, IEEE Transactions on Computers.

[3]  Mohsen Guizani,et al.  Exploiting Task Elasticity and Price Heterogeneity for Maximizing Cloud Computing Profits , 2018, IEEE Transactions on Emerging Topics in Computing.

[4]  Houman Homayoun,et al.  Managing distributed UPS energy for effective power capping in data centers , 2012, 2012 39th Annual International Symposium on Computer Architecture (ISCA).

[5]  George Kesidis,et al.  Pricing of service in clouds: optimal response and strategic interactions , 2014, PERV.

[6]  Mohsen Guizani,et al.  Peak shaving through optimal energy storage control for data centers , 2016, 2016 IEEE International Conference on Communications (ICC).

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

[8]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[9]  Nicola Cordeschi,et al.  FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method , 2014, Cluster Computing.

[10]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

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

[12]  Rajiv Ranjan,et al.  Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers , 2016, IEEE Systems Journal.