A Renewable Energy Driven Approach for Computational Sprinting
A Renewable Energy Driven Approach for Computational Sprinting
复制标题
可再生能源驱动的计算冲刺方法
DOI：
10.1109/tpds.2018.2890230
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发表时间：
2019-07
影响因子：
5.3
通讯作者：
Jiang Hong
中科院分区：
文献类型：
--
作者：
Cai Haoran;Cao Qiang;Jiang Hong
计算冲刺，通过打开所有处理器核心，并用某些相变材料吸收额外的散热，允许芯片暂时超过其功率和热量限制，已被证明是提高突发工作负载计算性能的有效方法。然而，可用于短跑的额外电力受到现有配电基础设施的限制。单独使用电池来提供额外的电力来实现性能目标，不仅限制了短跑的有效性，而且对电池的寿命也有负面影响。在绿色数据中心利用可再生电源提供了充分利用计算冲刺的机会。然而，可再生能源的间歇性，以及有限的冷却能力，使其非常具有挑战性。在本文中，我们提出了GreenSprint，一种可再生能源驱动的方法，使数据中心能够在可再生能源供应的间歇性和时变性下进行计算Sprint，从而有效地提高其计算性能。设计了三种基本策略来确定基于当前电源的短跑核心数和频率水平。此外，我们提出了一种结合强化学习的混合策略，以供电安全和服务质量为目标，动态确定最优服务器设置。考虑到实际的降温条件，我们还提出了一种温度感知的冲刺策略混合T。最后，我们构建了一个实验原型，在一个由10台服务器组成的集群上使用模拟的太阳能发电机来评估GreenSprint。结果表明，在可再生能源供应充足的情况下，可再生能源本身可以支持不同的短跑时长，并且对于典型的交互应用，可以将性能提高高达4.8倍；此外，我们还展示了在存在不同的可再生能源和有限的电池能量的情况下，核心计数和频率调节的有效性。
Computational Sprinting, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials, has proven to be an effective way to boost the computing performance for bursty workloads. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to make full use of Computational Sprinting. However, the intermittent nature of renewable energy, along with limited cooling capacity, makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting under the intermittent and time-varying nature of renewable energy supply. Three basic strategies are designed to determine the core count and frequency level for sprinting based on current power supply. Furthermore, we propose a Hybrid strategy that combines reinforcement learning to dynamically determine the optimal server setting, targeting at both the power provision safety and the quality of service. In consideration of practica cooling conditions, we also present a thermal-aware sprinting strategy Hybrid-T. Finally, we build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications.We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.
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DOI：
10.1145/2000064.2000108
发表时间：
2011-06
期刊：
2011 38th Annual International Symposium on Computer Architecture (ISCA)
影响因子：
--
作者：
H. Esmaeilzadeh;Emily R. Blem;Renée St. Amant;Karthikeyan Sankaralingam;D. Burger
通讯作者：
H. Esmaeilzadeh;Emily R. Blem;Renée St. Amant;Karthikeyan Sankaralingam;D. Burger
DOI：
10.1145/3007787.3001187
发表时间：
2016-06
期刊：
2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)
影响因子：
--
作者：
Qiang Wu;Qingyuan Deng;L. Ganesh;Chang-Hong Hsu;Yun Jin;Sanjeev Kumar;Bin Li;Justin Meza
通讯作者：
Qiang Wu;Qingyuan Deng;L. Ganesh;Chang-Hong Hsu;Yun Jin;Sanjeev Kumar;Bin Li;Justin Meza
DOI：
10.1109/hpca.2014.6835924
发表时间：
2014-06
期刊：
2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA)
影响因子：
--
作者：
Wenli Zheng;Kai Ma;Xiaorui Wang
通讯作者：
Wenli Zheng;Kai Ma;Xiaorui Wang
DOI：
10.1145/2451116.2451135
发表时间：
2013-03
期刊：
--
影响因子：
--
作者：
Arun Raghavan;Laurel Emurian;Lei Shao;M. Papaefthymiou;K. Pipe;T. Wenisch;Milo M. K. Martin
通讯作者：
Arun Raghavan;Laurel Emurian;Lei Shao;M. Papaefthymiou;K. Pipe;T. Wenisch;Milo M. K. Martin
DOI：
10.1145/2094091.2094105
发表时间：
2011-10
期刊：
--
影响因子：
--
作者：
Baris Aksanli;J. Venkatesh;L. Zhang;Tajana Simunic
通讯作者：
Baris Aksanli;J. Venkatesh;L. Zhang;Tajana Simunic