TY - GEN
T1 - Recouping energy costs from cloud tenants
T2 - 6th ACM International Conference on Future Energy Systems, e-Energy 2015
AU - Wang, Cheng
AU - Nasiriani, Neda
AU - Kesidis, George
AU - Urgaonkar, Bhuvan
AU - Wang, Qian
AU - Chen, Lydia Y.
AU - Gupta, Aayush
AU - Birke, Robert
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/7/14
Y1 - 2015/7/14
N2 - As energy costs become increasingly greater contributors to a cloud provider's overall costs, it is important for the cloud to recoup these energy costs from its tenants for profitability via appropriate pricing design. The poor predictability of real-world tenants' demand and demand responses (DRs) make such pricing design a challenging problem. We formulate a leader-follower game-based cloud pricing framework with the goal of maximizing cloud's profit. The key distinguishing aspect of our approach is our emphasis on modeling both the cloud and its tenants as working with low predictability in their inputs. Consequently, we model them as employing myopic control with short-term predictive models. Our empirical evaluation using tenant trace from IBM production data centers shows that (i) cloud's profit and VM prices are sensitive to the tradeoffs between its energy costs, tenant's demand and DR, and (ii) the cloud's estimation of tenants' demands/DR may significantly affect its profitability.
AB - As energy costs become increasingly greater contributors to a cloud provider's overall costs, it is important for the cloud to recoup these energy costs from its tenants for profitability via appropriate pricing design. The poor predictability of real-world tenants' demand and demand responses (DRs) make such pricing design a challenging problem. We formulate a leader-follower game-based cloud pricing framework with the goal of maximizing cloud's profit. The key distinguishing aspect of our approach is our emphasis on modeling both the cloud and its tenants as working with low predictability in their inputs. Consequently, we model them as employing myopic control with short-term predictive models. Our empirical evaluation using tenant trace from IBM production data centers shows that (i) cloud's profit and VM prices are sensitive to the tradeoffs between its energy costs, tenant's demand and DR, and (ii) the cloud's estimation of tenants' demands/DR may significantly affect its profitability.
UR - http://www.scopus.com/inward/record.url?scp=84961250869&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961250869&partnerID=8YFLogxK
U2 - 10.1145/2768510.2768541
DO - 10.1145/2768510.2768541
M3 - Conference contribution
AN - SCOPUS:84961250869
T3 - e-Energy 2015 - Proceedings of the 2015 ACM 6th International Conference on Future Energy Systems
SP - 141
EP - 150
BT - e-Energy 2015 - Proceedings of the 2015 ACM 6th International Conference on Future Energy Systems
PB - Association for Computing Machinery, Inc
Y2 - 14 July 2015 through 17 July 2015
ER -