基于Brownout和近似Markov决策过程的应用组件节能调度
Energy Efficient Scheduling of Application Components via Brownout and Approximate Markov Decision Process
摘要：
云数据中心中的意外负载可能会引发过载情况和性能下降。为了保证系统性能，要求云计算环境具有处理过载的能力。现有的方法，如动态电压频率调节和虚拟机整合，是有效的，在处理部分过载，但是，他们不能工作时，整个数据中心过载。Brownout已被证明是一种很有前途的方法，可以通过暂时停用应用程序非强制性组件或微服务来缓解过载。此外，已应用掉电来减少数据中心的能源消耗。研究表明，当一项或多项服务暂时不提供时，在节能和向用户提供的折扣（收入损失）之间存在权衡。在本文中，我们提出了一个基于掉电近似马尔可夫决策过程的方法来改善上述权衡。基于实际跟踪的结果表明，我们的方法比虚拟机整合方法节省20%的能源消耗。与现有的节能限电方法相比，我们的方法减少了给用户的折扣金额，同时节省了类似的能源消耗。
英文摘要：
Unexpected loads in Cloud data centers may trigger overloaded situation and performance degradation. To guarantee system performance, cloud computing environment is required to have the ability to handle overloads. The existing approaches, like Dynamic Voltage Frequency Scaling and VM consolidation, are effective in handling partial overloads, however, they cannot function when the whole data center is overloaded. Brownout has been proved to be a promising approach to relieve the overloads through deactivating application non-mandatory components or microservices temporarily. Moreover, brownout has been applied to reduce data center energy consumption. It shows that there are trade-offs between energy saving and discount offered to users (revenue loss) when one or more services are not provided temporarily. In this paper, we propose a brownout-based approximate Markov Decision Process approach to improve the aforementioned trade-offs. The results based on real trace demonstrate that our approach saves 20% energy consumption than VM consolidation approach. Compared with existing energy-efficient brownout approach, our approach reduces the discount amount given to users while saving similar energy consumption.
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