On the Energy Proportionality of Distributed NoSQL Data Stores

Subramaniam, Balaji; Feng, Wu-chun

doi:10.1007/978-3-319-17248-4_14

Cited by 7 publications

(10 citation statements)

References 21 publications

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“…Via this mechanism a user can specify a power consumption threshold that the processor will not exceed for a given period. Energy savings achieved by RAPL have been evaluated for data stores applications [5], and latency critical workloads [6]. This power capping tool offers better energy proportionality, but does not help reducing idle consumption.…”

Section: Related Work On Energy Proportionalitymentioning

confidence: 99%

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Villebonnet¹,

Costa²,

Lefèvre³

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

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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. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18804The contribution was presented at CLUSTER 2016 :http://www.ieeecluster2016.org/ Abstract-As the number of data centers increases, it is urgent to reduce their energy consumption. Although servers are becoming more energy-efficient, their idle consumption remains high, which is an issue as data centers are often over-provisioned. This work proposes a novel approach for building data centers with 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, by migrating applications and switching machines on or off, so that the energy consumed by the data center is proportional to the load. Experiments evaluate the approach and show the energy savings achieved by our heterogeneous data center design and management while satisfying Quality of Service (QoS) constraints.

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Section: Related Work On Energy Proportionalitymentioning

confidence: 99%

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Villebonnet¹,

Costa²,

Lefèvre³

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

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show abstract

“…Eqs. (17) and (18) are added constraints to the constraints set for applying the nonlinear programming algorithm. In this way, the bounded problem P b T min can be solved through adding the constraint P ≤ P b and using min T as the objective function.…”

Section: Nonlinear Programming Algorithmmentioning

confidence: 99%

Hot-N-Cold model for energy aware cloud databases

Guo

Pierson

Song

et al. 2019

Journal of Parallel and Distributed Computing

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h i g h l i g h t s • The energy efficiency in cloud database systems is discussed. • The resource-providing problem in cloud database systems is considered as two bounded problems which are studied in this work. • Under Hot-N-Cold model, nonlinear programming algorithm and multiphase algorithm are proposed to solve the bounded problems. • The simulation and real case experiments are designed and implemented to measure the proposed algorithms in aspect of performance and assurance.

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“…The energy savings achieved by RAPL have been evaluated facing different use-case applications, respectively data stores and latency critical workloads [5], [6]. This power capping technology offers better energy proportionality, but does not solve the problem of high idle consumption.…”

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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On the Energy Proportionality of Distributed NoSQL Data Stores

Cited by 7 publications

References 21 publications

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Hot-N-Cold model for energy aware cloud databases

Energy Proportionality in Heterogeneous Data Center Supporting Applications with Variable Load

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