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Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing

机译:基于性能的多核地理分布式云计算定价

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摘要

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem for cloud providers. CPU frequency scaling can be used to reduce power dissipation, but also impacts VM performance and therefore revenue. In this paper, we firstly propose a non-linear power model that estimates power dissipation of a multi-core CPU physical machine (PM) and secondly a pricing model that adjusts the pricing based on the VM’s CPU-boundedness characteristics. Finally, we present a cloud controller that uses these models to allocate VMs and scale CPU frequencies of the PMs to achieve energy cost savings that exceed service revenue losses. We evaluate the proposed approach using simulations with realistic VM workloads, electricity price and temperature traces and estimate energy savings of up to 14:57%.
机译:新的定价策略正在出现,云提供商根据分配的CPU频率对资源供应进行收费。结果,资源可以作为不同性能水平和价格的组合提供给用户,可以在运行时进行配置。有了这样的新定价方案以及数据中心不断增加的能源成本,在节能与性能和收入损失之间取得平衡对云提供商来说是一个具有挑战性的问题。 CPU频率缩放可用于减少功耗,但也会影响VM性能并因此影响收入。在本文中,我们首先提出一个非线性功率模型,该模型可估计多核CPU物理机(PM)的功耗,其次,该定价模型可根据VM的CPU边界特性来调整定价。最后,我们提出了一种云控制器,该云控制器使用这些模型来分配VM和扩展PM的CPU频率,以实现节省的能源成本,其成本超过了服务收入的损失。我们使用具有实际VM工作负载,电价和温度曲线的仿真评估提出的方法,并估计可节省多达14:57%的能源。

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