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Performance evaluation and dynamic optimization of speed scaling on web servers in cloud computing

机译：云计算中Web服务器速度扩展的性能评估和动态优化

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摘要

The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energyproportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is serviceoriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.
机译：如今，随着数据中心能源成本的不断增加，大型数据中心的能耗越来越受到关注，从而使增强的性能变得非常昂贵。就云计算的规模和性能而言，这已成为进一步发展的瓶颈。因此，减少数据中心的能耗已成为绿色IT和绿色计算的关键研究主题。提供云服务计算的Web服务器在不同情况下以各种速度运行。通过使用速度缩放在这些状态之间切换，能量消耗与工作量成比例，这被称为能量比例。这项研究使用随机服务决策网来研究Web服务器上的节能速度扩展。该模型将随机Petri网与Markov决策过程模型结合在一起。这使模型能够动态优化速度缩放策略并进行性能评估。该模型的图形化和直观性足以表征复杂的系统行为和决策。该模型是使用典型服务模式面向服务的，以将复杂模型简化为具有较小状态空间的简单模型。性能和奖励等价分析大大减少了系统行为子网。该模型提供了最佳策略，并更简洁地评估了性能和能耗指标。

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