A Dynamic Energy Conservation Scheme for Clusters in Computing Centers

Chen, Wenguang; Jiang, Feiyun; Zheng, Weimin; Zhang, Peinan

doi:10.1007/11599555_25

Cited by 2 publications

(1 citation statement)

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“…VOVO strategy has been studied for more than a decade. Many VOVO approaches [33,[35][36][37], which dynamically size a cluster according to a preset threshold of CPU utilization or resource usage, were developed based on the designs proposed by Chase et al [1] or Pinheiro et al [2]. To compare the proposed queueing-based approach with the threshold-based approaches, Pinheiro's approach [2] is simulated and denoted as vovo scenario.…”

Section: Analysis and Comparisonmentioning

confidence: 99%

Modeling and Analysis of Queueing-Based Vary-On/Vary-Off Schemes for Server Clusters

Tang

Dai

2015

Mathematical Problems in Engineering

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A cloud system usually consists of a lot of server clusters handling various applications. To satisfy the increasing demands, especially for the front-end web applications, the computing capacity of a cloud system is often allocated for the peak demand. Such installation causes resource underutilization during the off-peak hours. Vary-On/Vary-Off (VOVO) schemes concentrate workloads on some servers instead of distributing them across all servers in a cluster to reduce idle energy waste. Recent VOVO schemes adopt queueing theory to model the arrival process and the service process for determining the number of powered-on servers. For the arrival process, Poisson process can be safely assumed in web services due to the large number of independent sources. On the other hand, the heavy-tailed distribution of service times is observed in real web systems. However, there are no exact solutions to determine the performance forM/heavy-tailed/mqueues. Therefore, this paper presents two queueing-based sizing approximations for Poisson and non-Poisson governed service processes. The simulation results of the proposed approximations are analyzed and evaluated by comparing with the simulated system running at full capacity. This relative measurement indicates that the Pareto distributed service process may be adequately modeled by memoryless queues when VOVO schemes are adopted.

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Section: Analysis and Comparisonmentioning

confidence: 99%

Modeling and Analysis of Queueing-Based Vary-On/Vary-Off Schemes for Server Clusters

Tang

Dai

2015

Mathematical Problems in Engineering

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DVFS Aware CPU Credit Enforcement in a Virtualized System

et al. 2013

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Part 2: Cloud ComputingInternational audienceNowadays, virtualization is present in almost all computing infrastructures. Thanks to VM migration and server consolidation, virtualization helps reducing power consumption in distributed environments. On another side, Dynamic Voltage and Frequency Scaling (DVFS) allows servers to dynamically modify the processor frequency (according to the CPU load) in order to achieve less energy consumption. We observed that these two techniques have several incompatibilities. For instance, if two virtual machines VM1 and VM2 are running on the same physical host (with their respective allocated credits), VM1 being overloaded and VM2 being underloaded, the host may be globally underloaded leading to a reduction of the processor frequency, which would penalize VM1 even if VM1's owner booked a given CPU capacity. In this paper, we analyze the compatibility of available VM schedulers with DVFS management in virtualized environments, we identify key issues and finally propose a DVFS aware VM scheduler which addresses these issues. We implemented and evaluated our prototype in the Xen virtualized environment

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A Dynamic Energy Conservation Scheme for Clusters in Computing Centers

Cited by 2 publications

References 12 publications

Modeling and Analysis of Queueing-Based Vary-On/Vary-Off Schemes for Server Clusters

Modeling and Analysis of Queueing-Based Vary-On/Vary-Off Schemes for Server Clusters

DVFS Aware CPU Credit Enforcement in a Virtualized System

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