[hpc-announce] [Call for Papers][Energies]: Special Issue "Cloud Computing Systems and Energy Efficient Utilization"

Alexey Lastovetsky alexey.lastovetsky at ucd.ie
Fri May 15 07:58:37 CDT 2020

Energies Special Issue Information

Dear Colleagues,

In the current digital era, computing is prevalent, and the level of
penetration of computing in society, industry, economy, science, and
engineering will only increase. Therefore, the energy involved in
computing—which was not considered a big issue in the past—is becoming a
grand technological challenge. The proportion of total energy consumption
represented by ICT is rapidly increasing. By 2030, it is predicted to
consume around 50% of all the generated electricity, according to some
prognoses. This situation is not sustainable, and extraordinary efforts are
needed to increase energy efficiency in ICT.

Cloud computing systems have demonstrated exponential growth over the last
decade and quickly become the dominant computing infrastructure. By the end
of 2020, two-thirds of enterprise infrastructure will be cloud-based;
additionally, 82% of the workload will reside in the cloud
(https://techjury.net/stats-about/cloud-computing/). Therefore, any
improvements in the energy efficiency of cloud systems will also improve the
global energy efficiency of computing.

This Special Issue invites submissions addressing all aspects of energy
efficiency in cloud computing systems at all levels. Manuscripts reporting
accurate application-level power and energy predictive models, and energy
measurement methods and tools are particularly welcome. Methods on the
optimization of multiple objectives are also welcome but must include energy
consumption as one of the primary objectives.

Alexey Lastovetsky
Guest Editor

Energies (ISSN 1996-1073; CODEN: ENERGA) is a peer-reviewed open access
journal of related scientific research, technology development, engineering,
and the studies in policy and management and is published semi-monthly
online by MDPI.
Impact Factor: 2.707 (2018) ; 5-Year Impact Factor: 2.990 (2018)

Open Access free for readers, with article processing charges (APC) paid by
authors or their institutions.
High visibility: indexed by the Science Citation Index Expanded (Web of
Science), Ei Compendex, Scopus and other databases.
Rapid publication: manuscripts are peer-reviewed and a first decision
provided to authors approximately 17.1 days after submission; acceptance to
publication is undertaken in 2.9 days (median values for papers published in
this journal in the second half of 2019).

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and
logging in to this website. Once you are registered, click here to go to the
submission form. Manuscripts can be submitted until the deadline. All papers
will be peer-reviewed. Accepted papers will be published continuously in the
journal (as soon as accepted) and will be listed together on the special
issue website. Research articles, review articles as well as short
communications are invited. For planned papers, a title and short abstract
(about 100 words) can be sent to the Editorial Office for announcement on
this website.

Submitted manuscripts should not have been published previously, nor be
under consideration for publication elsewhere (except conference proceedings
papers). All manuscripts are thoroughly refereed through a single-blind
peer-review process. A guide for authors and other relevant information for
submission of manuscripts is available on the Instructions for Authors page.
Energies is an international peer-reviewed open access semimonthly journal
published by MDPI.

Please visit the Instructions for Authors page before submitting a
manuscript. The Article Processing Charge (APC) for publication in this open
access journal is 1800 CHF (Swiss Francs). Submitted papers should be well
formatted and use good English. Authors may use MDPI's English editing
service prior to publication or during author revisions.

energy of computing
cloud computing
energy efficient computing
energy predictive models
measurement of energy of computing
Published Papers
This special issue is now open for submission.

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