SizeCap: Efficiently handling power surges in fuel cell powered data centers

Abstract

Fuel cells are a promising power source for future data centers, offering high energy efficiency, low greenhouse gas emissions, and high reliability. However, due to mechanical limitations related to fuel delivery, fuel cells are slow to adjust to sudden increases in data center power demands, which can result in temporary power shortfalls. To mitigate the impact of power shortfalls, prior work has proposed to either perform power capping by throttling the servers, or to leverage energy storage devices (ESDs) that can temporarily provide enough power to make up for the shortfall while the fuel cells ramp up power generation. Both approaches have disadvantages: power capping conservatively limits server performance and can lead to service level agreement (SLA) violations, while ESD-only solutions must significantly overprovision the energy storage device capacity to tolerate the shortfalls caused by the worstcase (i.e., largest) power surges, which greatly increases the total cost of ownership (TCO).We propose SizeCap, the first ESD sizing framework for fuel cell powered data centers, which coordinates ESD sizing with power capping to enable a cost-effective solution to power shortfalls in data centers. SizeCap sizes the ESD just large enough to cover the majority of power surges, but not the worst-case surges that occur infrequently, to greatly reduce TCO. It then uses the smaller capacity ESD in conjunction with power capping to cover the power shortfalls caused by the worst-case power surges. As part of our new flexible framework, we propose multiple power capping policies with different degrees of awareness of fuel cell and workload behavior, and evaluate their impact on workload performance and ESD size. Using traces from Microsoft's production data center systems, we demonstrate that SizeCap significantly reduces the ESD size (by 85% for a workload with infrequent yet large power surges, and by 50% for a workload with frequent power surges) without violating any SLAs.

Hipster: Hybrid Task Manager for Latency-Critical Cloud Workloads

“…Recent works [15][16][17][21][22][23] have shown that traditional power management practices and CPU utilization measures are unsuitable to drive task management for data center workloads. This is because prior schemes (like OS-level DVFS) work well to deliver long-term performance for batch workloads, but they can severely hurt the QoS of latency-critical data center workloads.…”

A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers

“…Zhou et al [30] first tried to quantitatively analyze the benefits of fuel cell power generation, and explained how to realize intelligent coordination between power grid in data center networks and fuel cell power generation. Li et al [9] first proposed an ESD classification framework for data centers powered by fuel cells. A variety of power capping strategies with different degrees of knowledge of fuel cells and workload behavior are introduced to evaluate the effect on workload performance and ESD size.…”

“…On the one hand, as one alternative green energy resource, fuel cells have emerged as a promising energy source for data centers due to their advantages of high energy efficiency, high reliability, and low carbon dioxide emission [7,8]. Although fuel cells show many advantages, they are slow in changing the output power [9], i.e., it may take a few minutes to reach the energy requirement. In order to address this challenge, some research was proposed introducing energy storage devices to reduce the effect of limited load following, which may add extra costs [10,11].…”