Benefits and limitations of tapping into stored energy for datacenters

Abstract

Abstract. Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this paper, we present a novel knob of energy buffer (eBuff) available in the form of UPS batteries in datacenters for this cost optimization. Intuitively, eBuff stores energy in UPS batteries during "valleys" -periods of lower demand, which can be drained during "peaks" -periods of higher demand. UPS batteries are normally used as a fail-over mechanism to transition to captive power sources upon utility failure. Furthermore, frequent discharges can cause UPS batteries to fail prematurely. We conduct detailed analysis of battery operation to figure out feasible operating regions given such battery lifetime and datacenter availability concerns. Using insights learned from this analysis, we develop peak reduction algorithms that combine the UPS battery knob with existing throttling based techniques for minimizing datacenter power costs. Using an experimental platform, we offer insights about Op-ex savings offered by eBuff for a wide range of workload peaks/valleys, UPS provisioning, and application SLA constraints. We find that eBuff can be used to realize 15-45% peak power reduction, corresponding to 6-18% savings in Op-ex across this spectrum. eBuff can also play a role in reducing Cap-ex costs by allowing tighter overbooking of power infrastructure components and we quantify the extent of such Cap-ex savings. To our knowledge, this is the first paper to exploit stored energy -typically lying untapped in the datacenter -to address the peak power draw problem.

Sustainable Cloud Computing

“…We devise an online solution based on T-slot Lyapunov optimization to optimize both the electricity cost, and the peak power cost without significantly violating the carbon capping requirement of the data centers. Finally, while the existing work on data center peak power optimization rely on predictability of cloud parameters [47,50,51,52,61,75,116,121], we show that the prediction error of cloud parameters such as workload has a very harmful impact on the peak power optimization, and adopt stochastic programming to remove such an impact. The following sections give a detailed overview on the contributions.…”

Multi-tier energy buffering management for IDCs with heterogeneous energy storage devices

“…Typically charging rate is much less than discharging rate, e.g., charging rate is 5-10 times less than discharging for lead-acid batteries. As suggested by the previous studies [4], [8], the existing UPSes in data centers can be utilized for energy management of the tier one. However, the energy level at the battery should always be sufficient to guarantee the desired availability, denoted by B min .…”

“…Particularly, Urgaonkar et al develop an on-line control algorithm using Lyponov optimization to exploit UPS devices to reduce cost in data centers [3]. Govindan et al perform a comprehensive study on the feasibility of utilizing UPS to store low-cost energy, the constraints (e.g., charging discharging periods depending on life-cycle of batteries) and a Markovian based solution to schedule batteries [4]. Authors in [6] perform a trace-based simulation using Akamai CDN workload traces to investigate the energy cost saving that can be achieved by using batteries to shave the peak power draw from the grid.…”

“…Further, researchers have suggested energy buffering using energy storage devices (ESD) such as batteries and ultra-capacitors [3]- [9]. Current data centers mainly use ESDs as power backup [4], [7]. These ESDs can also be used to store excess renewable energy and low-cost electricity during workload valleys, which can be used to meet high power demands [4]- [8].…”

“…Current data centers mainly use ESDs as power backup [4], [7]. These ESDs can also be used to store excess renewable energy and low-cost electricity during workload valleys, which can be used to meet high power demands [4]- [8]. However, energy buffering management should be aware of: (a) the intermittent nature of available energy from renewables, (b) variability in workload, (c) variability in the electricity pricing, and (d) heterogeneity in ESDs in terms of energy storage efficiency, discharge rate and storage capacity.…”