The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A data-value-driven adaptation framework for energy efficiency for data intensive applications in clouds
Abstract
The emerging of cloud computing and Big Data has been presenting to the world both grand opportunities and challenges. However, the increasing trend in energy consumption in clouds due to the fast growing quantity of data to be transmitted and processed has made cloud computing, together with Big Data phenomenon, becoming the dominant contributor in energy consumption, and consequently in CO2emission. In this paper, we propose an adaptation framework for data-intensive applications aiming to improve energy efficiency. The adaptation mechanism is driven by the data value extracted from datasets or data streams of the applications. Our main contribution lies in the proposal of treating large amount of data according to their value, i.e., their level of importance.
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“To decrease the power usage of data intensive applications in cloud data centers, the authors in [31] have presented an adoption framework for the data intensive applications whose primary goal is to minimize energy usage.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [31]
Citations
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Self-citation
Research works printed but not linked: 6
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Cited by
Sorted by citations
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“To decrease the power usage of data intensive applications in cloud data centers, the authors in [31] have presented an adoption framework for the data intensive applications whose primary goal is to minimize energy usage.”
Metadata
- Authors
- Ho, T. T. N., & Pernici, B.
- Affiliations
- Politecnico di Milano (IT)
- ORCID
- Pernici, Barbara
- Venue
- 2015 IEEE Conference on Technologies for Sustainability (SusTech)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 47-52
- OpenAlex
- W1930800258
- Cited by
- 15
- References
- 19
- Citations by year
- 2026: 1 · 2023: 2 · 2022: 2 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 2
- FWCI
- 1.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Technologies for Sustainability (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- data value, data-intensive applications, dynamic adaptation, data streams, CO2 emissions, big data, data importance, energy consumption
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