Monitoring and Assessing Energy Consumption and CO2 Emissions in Cloud-Based Systems

Cappiello, Cinzia; Datre, Sumit; Fugini, Mariagrazia; Melià, Paco; Pernici, Barbara; Plebani, Pierluigi; Gienger, Michael; Tenschert, Axel

doi:10.1109/smc.2013.29

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…On the other hand, Greenness metrics (Green Performance Indicator, Carbon Emission Calculator, emission factors (gCO2e/kWh etc. [56]) are directly involved into CO 2 emission. Understanding these metrics provide a better view on how EE can be achieved in wireless networks from a component, equipment and networks level, respectively [57].…”

Section: Green Metricsmentioning

confidence: 99%

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

Mahapatra¹,

Nijsure

Kaddoum

et al. 2016

IEEE Commun. Surv. Tutorials

199

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Abstract-Energy efficient (EE) communication has earnedtremendous interest in recent years due to ever increasing number of wireless devices operating in shrinking cells, while demanding high data rates with high Quality of Services (QoS) and Quality of Expectation (QoE). To support these objectives, energy is consumed in every protocol layer. Establishing and maintaining a successful wireless communication link to simultaneously achieve all these objectives becomes challenging since the energy consumption requirements of the user and network are different for different objectives. Thus, there is a need for tradeoff techniques to achieve energy efficiency in each protocol layer. In this paper, we provide a survey of different tradeoff mechanisms proposed in the literature. The EE tradeoffs have been classified based on each protocol layer and discussed its affect in the network energy efficiency. These other QoS parameters include spectral efficiency, deployment, delay, routing, scheduling, bandwidth and coding etc. This survey also discusses the various EE techniques to improve energy-efficiency in infrastructure mode. Finally, the work provides an discussion, where impact of EE tradeoffs have been presented based on different wireless architecture towards realizing a green wireless communication network.

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Section: Green Metricsmentioning

confidence: 99%

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

Mahapatra¹,

Nijsure

Kaddoum

et al. 2016

IEEE Commun. Surv. Tutorials

199

View full text Add to dashboard Cite

show abstract

“…In other words, this type of studies could be concerned with the overall workloads from numerous and various applications. (5) Publications that model the environmental hardware's energy consumption by notating applications' (or application components') energy consumption (e.g., [18]). This type of studies were not in the context of a single application (component) scenario, either.…”

Section: Exclusion Criteriamentioning

confidence: 99%

A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates

Zheng

Tesfatsion

Bastani

et al. 2017

IEEE Trans. Sustain. Comput.

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Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. There- fore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.

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“…A measure for greenness defined at different system layers to identify the reasons for power waste. Further an infrastructure is also required for assessing the set of metrics [59].…”

Section: Related Workmentioning

confidence: 99%

Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review

Uddin

Darabidarabkhani

Shah

et al. 2015

Renewable and Sustainable Energy Reviews

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Monitoring and Assessing Energy Consumption and CO2 Emissions in Cloud-Based Systems

Cited by 11 publications

References 17 publications

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates

Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review

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