Power awareness and power management techniques emerged as an interesting topic in the last decade in both cloud computing and fog computing scenarios. In this context, several emerging technologies are opening novel research challenges that should put together both power management and performance of the running workloads. In this context, energy proportionality can help those systems to match power and performance needs. In this paper we introduce E2 ASY, which aims at building an energy proportionality toolbox for cloud and embedded fog systems that provides solutions able to observe performance and power and managing them towards the desired goals. Preliminary results obtained on cloud systems shows negligible overhead on the monitored system and good results on power consumption reduction.

Energy Efficiency for Autonomic Scalable Systems: Research Objectives and Preliminary Results

Brondolin, R.;Arnaboldi, M.;SARDELLI, TOMMASO DONATO;Notargiacomo, S.;Santambrogio, M. D.
2018

Abstract

Power awareness and power management techniques emerged as an interesting topic in the last decade in both cloud computing and fog computing scenarios. In this context, several emerging technologies are opening novel research challenges that should put together both power management and performance of the running workloads. In this context, energy proportionality can help those systems to match power and performance needs. In this paper we introduce E2 ASY, which aims at building an energy proportionality toolbox for cloud and embedded fog systems that provides solutions able to observe performance and power and managing them towards the desired goals. Preliminary results obtained on cloud systems shows negligible overhead on the monitored system and good results on power consumption reduction.
IEEE 4th International Forum on Research and Technologies for Society and Industry, RTSI 2018 - Proceedings
9781538662823
Autonomic power management; cloud computing; fog computing; ODA loop; Artificial Intelligence; Computer Networks and Communications; Computer Science Applications1707 Computer Vision and Pattern Recognition; Energy Engineering and Power Technology; Renewable Energy, Sustainability and the Environment; Industrial and Manufacturing Engineering; Instrumentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1074783
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