Trends and effects of energy proportionality on server provisioning in data centers

Varsamopoulos, Georgios; Abbasi, Zahra; Gupta, Sandeep

doi:10.1109/hipc.2010.5713198

Cited by 37 publications

(27 citation statements)

References 17 publications

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“…These locations correspond to the location of three major Google data centers. We used the historical electricity prices for the above locations [117] (see Fig. 5.3).…”

Section: Discussionmentioning

confidence: 99%

“…It is possible to design thermal aware server and workload management schemes to avoid the cooling-computing power tradeoff [16,44,117]. In general, the active server set selection affects the total power of the data center due to the non-uniform temperature distribution in the room (because servers do not equally impact the temperature in the room, nor are the airflow patterns symmetric) and the servers' heterogeneity in terms of their power and computing performance.…”

Section: (B) According To the Recent Uptimementioning

confidence: 99%

“…TACOMA [16,17,117] of the workload. TACOMA is a multi-tier energy management scheme considering the long and short term aviation of the workload, and it is shown to reap the energy usage benefits from the inherent variabilities of workload at both long and short term.…”

Section: Peak Power Reduction and Energy Bufferingmentioning

confidence: 99%

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Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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Energy consumption of the data centers worldwide is rapidly growing fueled by everincreasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. In order to achieve these objectives, this dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level.This dissertation devises optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and the cooling power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.

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“…These locations correspond to the location of three major Google data centers. We used the historical electricity prices for the above locations [117] (see Fig. 5.3).…”

Section: Discussionmentioning

confidence: 99%

Section: (B) According To the Recent Uptimementioning

confidence: 99%

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Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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“…This is an issue because typical servers are not very energy efficient under low utilization. In [4], Varsamopoulos et al define two metrics to quantify energy proportionality: IPR, for Ideal to Peak Ratio, which measures the dynamic power range, and LDR, for Linear Deviation Ratio, to evaluate the linearity of the consumption. They studied the evolution of energy proportionality and found that recent servers feature better characteristics, but most time it only concerns one aspect: a larger dynamic power range or an improved linearity.…”

Section: Related Work On Energy Proportionalitymentioning

confidence: 99%

Energy Proportionality in Heterogeneous Data Center Supporting Applications with Variable Load

Villebonnet¹,

Costa²,

Lefèvre³

et al. 2016

2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-The increasing number of data centers raises serious concerns regarding their energy consumption. Although servers have become more energy-efficient over time, their idle consumption remains high, which is an issue as resources in data centers are often over-provisioned. This work proposes a novel approach for building data centers so that their energy consumption is proportional to load. A data center hence comprises heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time and design a scheduler that dynamically reconfigures the infrastructure to minimize its energy consumption according to current load and application requirements. Based on load forecasts, it takes reconfiguration decisions and performs actions such as migrating applications and switching machines on or off. The approach is evaluated considering a data center with heterogeneous resources, and the experiments show how to adjust the parameters of scheduling policies to save the most energy while satisfying Quality of Service (QoS) constraints.

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“…There are research studies that try to map the yeilded curve to a polynomial, most usually a linear function. Although a linear model is not always accurate [45] it has been extensively used due to its simplicity.…”

Section: Characterizing G C I Functionmentioning

confidence: 99%