SmartDPSS: Cost-Minimizing Multi-source Power Supply for Datacenters with Arbitrary Demand
SmartDPSS: Cost-Minimizing Multi-source Power Supply for Datacenters with Arbitrary Demand
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DOI：
10.1109/icdcs.2013.59
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Wei Deng;Fangming Liu;Hai Jin;Chuan Wu
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文献类型：
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作者：
Wei Deng;Fangming Liu;Hai Jin;Chuan Wu
为了应对飙升的电力成本、大量的碳排放和意外停电，云服务提供商(CSP)通常为其数据中心配备由多种来源培育的电源系统(DPS)：(1)具有时变电价的智能电网，(2)不间断电源(UPS)，以及(3)具有间歇和不确定供应的可再生能源。如何在多个电源之间以互补的方式运行，随着时间的推移向具有任意需求的数据中心用户提供可靠的能量，同时从长远来看将CSP的运营成本降至最低，这仍然是一个重大挑战。提出了一种基于双时间尺度Lyapunov优化技术的DPSS在线控制算法SmartDPSS。SmartDPSS不需要系统统计的先验知识，允许CSP在线决定每次服务多少电力需求，包括对延迟敏感的需求和容忍延迟的需求，从长期提前和实时电网市场购买的电量，以及一段时间内UPS的充放电，以便充分利用电网市场可用的可再生能源和时变价格，以实现最低运营成本。通过严格的理论分析，对在线控制算法的性能进行了深入的分析。我们还使用基于一个月的现场电力系统跟踪的大量仿真，从运营成本、需求服务延迟、数据中心可用性、系统健壮性和可扩展性等方面论证了该算法的最优性。
To tackle soaring power costs, significant carbon emission and unexpected power outage, Cloud Service Providers (CSPs) typically equip their Datacenters with a Power Supply System (DPSS) nurtured by multiple sources: (1) smart grid with time-varying electricity prices, (2) uninterrupted power supply (UPS), and (3) renewable energy with intermittent and uncertain supply. It remains a significant challenge how to operate among multiple power supply sources in a complementary manner, to deliver reliable energy to datacenter users with arbitrary demand over time, while minimizing a CSP's operation cost over the long run. This paper proposes an efficient, online control algorithm for DPSS, SmartDPSS, based on the two-timescale Lyapunov optimization techniques. Without requiring a priori knowledge of system statistics, SmartDPSS allows CSPs to make online decisions on how much power demand, including delay-sensitive demand and delay-tolerant demand, to serve at each time, the amount of power to purchase from the long-term-ahead and realtime grid markets, and charging and discharging of UPS over time, in order to fully leverage the available renewable energy and time-varying prices from the grid markets, for minimum operational cost. We thoroughly analyze the performance of our online control algorithm with rigorous theoretical analysis. We also demonstrate its optimality in terms of operational cost, demand service delay, datacenter availability, system robustness and scalability, using extensive simulations based on one-month worth of traces from live power systems.