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Power-performance tradeoffs in data center servers: DVFS, CPU pinning, horizontal, and vertical scaling

机译:数据中心服务器中的功率性能折衷:DVFS,CPU固定,水平和垂直缩放

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摘要

AbstractDynamic Voltage and Frequency Scaling (DVFS), CPU pinning, horizontal, and vertical scaling, are four techniques that have been proposed as actuators to control the performance and energy consumption on data center servers. This work investigates the utility of these four actuators, and quantifies the power-performance tradeoffs associated with them. Using replicas of the German Wikipedia running on our local testbed, we perform a set of experiments to quantify the influence of DVFS, vertical and horizontal scaling, and CPU pinning on end-to-end response time (average and tail), throughput, and power consumption with different workloads. Results of the experiments show that DVFS rarely reduces the power consumption of underloaded servers by more than 5%, but it can be used to limit the maximal power consumption of a saturated server by up to 20% (at a cost of performance degradation). CPU pinning reduces the power consumption of underloaded server (by up to 7%) at the cost of performance degradation, which can be limited by choosing an appropriate CPU pinning scheme. Horizontal and vertical scaling improves both the average and tail response time, but the improvement is not proportional to the amount of resources added. The load balancing strategy has a big impact on the tail response time of horizontally scaled applications.HighlightsThe impact of DVFS on the power consumption of underloaded servers is limited.vCPU consolidation reduces the power consumption at a cost of performance degradation.Combining VM scaling with consolidation of virtual CPUs improves energy efficiency.A load balancing strategy affects a tail latency of horizontally scaled applications.
机译: 摘要 动态电压和频率缩放(DVFS),CPU固定,水平和垂直缩放是被提议为控制执行器性能和能耗的四种技术。数据中心服务器。这项工作研究了这四个执行器的效用,并量化了与它们相关的功率性能折衷。使用在本地测试床上运行的德语维基百科的副本,我们执行了一组实验,以量化DVFS,垂直和水平缩放以及CPU固定对端到端响应时间(平均和尾部),吞吐量和不同工作负载下的功耗。实验结果表明,DVFS很少会将负载不足的服务器的功耗降低超过5%,但可以将饱和服务器的最大功耗降低20%(以降低性能为代价)。 CPU固定可降低性能欠佳的服务器的负载,从而降低欠载服务器的功耗(最多降低7%),这可以通过选择适当的CPU固定方案来加以限制。水平和垂直缩放可改善平均响应时间和尾部响应时间,但这种改善与添加的资源量不成比例。负载平衡策略对水平扩展应用程序的尾部响应时间有很大影响。 突出显示 DVFS对负载不足服务器功耗的影响是有限的。 vCPU整合可降低功耗,但会降低性能。 将VM扩展与虚拟CPU整合相结合可提高能源效率cy。 负载均衡策略会影响水平缩放应用程序的尾部延迟。

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