logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Software Development Lifecycle for Energy Efficiency: Techniques and Tools

delete2019-08-30
delete25
PRE
AI
S
Stefanos Georgiou *
S
Stamatia Rizou
D
Diomidis Spinellis
DOI：10.1145/3337773delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Motivation: In modern IT systems, the increasing demand for computational power is tightly coupled with ever higher energy consumption. Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level. Nevertheless, the software itself provides numerous opportunities for improving energy efficiency. Goal: Given that energy efficiency for IT systems is a rising concern, we investigate existing work in the area of energy-aware software development and identify open research challenges. Our goal is to reveal limitations, features, and tradeoffs regarding energy-performance for software development and provide insights on existing approaches, tools, and techniques for energy-efficient programming. Method: We analyze and categorize research work mostly extracted from top-tier conferences and journals concerning energy efficiency across the software development lifecycle phases. Results: Our analysis shows that related work in this area has focused mainly on the implementation and verification phases of the software development lifecycle. Existing work shows that the use of parallel and approximate programming, source code analyzers, efficient data structures, coding practices, and specific programming languages can significantly increase energy efficiency. Moreover, the utilization of energy monitoring tools and benchmarks can provide insights for the software practitioners and raise energy-awareness during the development phase.
Keywords:
GreenIT
energy efficiency
energy optimization
energy profiling
design patterns
parallel programming
code refactoring
source code analysis
coding practices
approximate programming
software development lifecycle
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

ACM Computing Surveys cover
ACM Computing Surveys
IF:
28
Papers:
2.4K
Citations:
3.5W

Organization

No organization information available