A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-off Debates
August 02, 2017 ยท The Cartographer ยท ๐ IEEE Transactions on Sustainable Computing
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and"
Evidence collected by the PWNC Scanner
Authors
Zheng Li, Selome Tesfatsion, Saeed Bastani, Ahmed Ali-Eldin, Erik Elmroth, Maria Kihl, Rajiv Ranjan
arXiv ID
1708.00777
Category
cs.DC: Distributed Computing
Citations
29
Venue
IEEE Transactions on Sustainable Computing
Last Checked
2 days ago
Abstract
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. Therefore, 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Distributed Computing
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
๐ป
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
๐ป
Ghosted
Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems
R.I.P.
๐ป
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
๐ป
Ghosted