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On the Carbon Footprint Optimization in an InterCloud Environment

机译:关于InterCloud环境中的碳足迹优化

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In this paper, we address the problem of Virtual Machine (VM) placement in an InterCloud with regard to the reduction of carbon footprint in such computing environment. In order to minimize data centers’ Greenhouse Gas (GhG) emissions, this paper proposes a mathematical formulation, where the placement approach is stated as a Mixed-Integer Nonlinear Programming (MINLP) problem which aims at minimizing the carbon footprint of the InterCloud. The proposed formulation presents an accurate carbon footprint evaluation based on joint optimization techniques, such as smart and performance-aware workload consolidation, along with cooling efficiency maximization, while considering the greenness factor of the data centers and the dynamic behavior of the equipment cooling fans. Simulations with an exact method showed that, compared with other baseline approaches, the proposed mathematical model leads to optimal configurations with minimal GhG emissions in a single cloud, as well as in an InterCloud environment, and can yield savings of up to 65 percent. The proposed model has also been compared with an approach that performs blind VM consolidation, and the obtained results showed that such model can simultaneously lead to VM configuration that yields the minimum carbon footprint while performing smart VM consolidation in order to prevent Service Level Agreement (SLA) violations.
机译:在本文中,我们针对减少此类计算环境中的碳足迹来解决在InterCloud中放置虚拟机(VM)的问题。为了最大程度地减少数据中心的温室气体(GhG)排放,本文提出了一种数学公式,该布局方法被称为混合整数非线性规划(MINLP)问题,旨在最小化InterCloud的碳足迹。提议的公式基于联合优化技术(例如智能和性能感知的工作负载合并)以及制冷效率最大化,提供了准确的碳足迹评估,同时考虑了数据中心的绿色因素和设备冷却风扇的动态行为。使用精确方法进行的仿真表明,与其他基准方法相比,所提出的数学模型可以在单个云以及InterCloud环境中以最小的GhG排放实现最佳配置,并且可以节省多达65%的成本。提议的模型还与执行盲虚拟机合并的方法进行了比较,获得的结果表明,该模型可以同时导致进行虚拟机配置,从而在执行智能虚拟机合并时产生最小的碳足迹,从而防止服务水平协议(SLA) )违规。

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