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Recouping Energy Costs From Cloud Tenants: Tenant Demand Response Aware Pricing Design

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Wang Cheng, Nasiriani Neda, Kesidis George, Urgaonkar Bhuvan, Wang Qian, Chen Lydia Y., Gupta Aayush, Birke Robert,
Project LoadOpt - Workload Characterization and Optimization for Multicore Systems
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Proceedings (peer-reviewed)

Title of proceedings 2015 ACM Sixth International Conference on Future Energy Systems (e-Energy)
Place Bangalore, India
DOI 10.1145/2768510.2768541


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.