A green program lifecycle supporting energy-efficient applications
December 23, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nadia Gamez, Jose-Miguel Horcas, Monica Pinto, Lidia Fuentes
arXiv ID
1612.08073
Category
cs.SE: Software Engineering
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
With the advent of the Internet of Things (IoT), the percentage of global emissions attributable to Information Systems is expected to further increase in the coming years, due to a proliferation of Internet-connected devices omnipresent in our daily lives (e.g., electric meters, wearable devices, etc.). Although software systems do not directly consume energy, they strongly affect the energy consumption of the hardware. So, developers should be more aware of the energy consumed by these systems during their lifetime, and think about the long-term consequences in the sustainability of our planet Earth. Indeed, once deployed, the energy consumed by a system depends on several factors determined mainly by the usage context. This means that the area of energy-efficient software development needs green development lifecycles that provide appropriate methodologies and tools to identify and analyze the energy hotspots of applications early at design time, and see how they can be self-adapted to the runtime context usage. Regrettably, there is a narrow view of developers and users and their responsibility in the energy consumed during application execution. Developers rarely address energy efficiency as some recent studies show, mostly because they lack appropriate methodologies and tools that help them to produce green software at runtime. So, although software energy efficiency is becoming increasingly important in an ever more technology-dependent world, development processes of self-greening systems supported by tools are still in their infancy. On the other hand, considering that many of current applications are normally deployed in smartphones or in any kind of smart objects (e.g., sensors, watches, etc.), optimizing the energy consumption during the execution will also have a strong impact in battery saving, enhancing the quality of experience of final users.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted