A Case for Sustainability and Environment Friendliness in Software
Development and Architecture Decisions by Taking Energy-Efficient Design
Decisions
- URL: http://arxiv.org/abs/2311.01680v1
- Date: Fri, 3 Nov 2023 03:05:19 GMT
- Title: A Case for Sustainability and Environment Friendliness in Software
Development and Architecture Decisions by Taking Energy-Efficient Design
Decisions
- Authors: Kaushik Dutta, Debra Vandermeer
- Abstract summary: We show potential for significant energy savings through energy-conscious choices at software development and selection time.
Data center energy consumption is estimated to account for 1% to 1.5% of all energy consumption worldwide.
- Score: 0.22252684361733285
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: IT power usage is a significant concern. Data center energy consumption is
estimated to account for 1% to 1.5% of all energy consumption worldwide.
Hardware designers, data center designers, and other members of the IT
community have been working to improve energy efficiency across many parts of
the IT infrastructure; however, little attention has been paid to the energy
efficiency of software components. Indeed, energy efficiency is currently not a
common performance criteria for software. In this work, we attempt to quantify
the potential for gains in energy efficiency in software, based on a set of
examples drawn from common, everyday decisions made by software developers and
enterprise architects. Our results show that there is potential for significant
energy savings through energy-conscious choices at software development and
selection time, making the software and IT artifact sustainable and environment
friendly.
Related papers
- Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
quantifying the energy footprint of a software system is one of the most basic activities.
This document aims to be a starting point for researchers who want to begin conducting work in this area.
arXiv Detail & Related papers (2024-07-16T11:21:30Z) - Open Energy Services -- Forecasting and Optimization as a Service for
Energy Management Applications at Scale [0.6495316960934344]
We promote an approach to split the complex optimization algorithms employed by energy management systems into standardized components.
This work is centered around the systematic design of a framework supporting the efficient implementation and operation of such forecasting and optimization services.
It describes the implementation of the design concept which we release under the name emphEnergy Service Generics as a free and open source repository.
arXiv Detail & Related papers (2024-02-23T09:46:49Z) - Heuristics and Metaheuristics for Dynamic Management of Computing and
Cooling Energy in Cloud Data Centers [0.0]
We propose novel power and thermal-aware strategies and models to provide joint cooling and computing optimizations.
Our results show that the combined awareness from both metaheuristic and best fit decreasing algorithms allow us to describe the global energy into faster and lighter optimization strategies.
This approach allows us to improve the energy efficiency of the data center, considering both computing and cooling infrastructures, in up to a 21.74% while maintaining quality of service.
arXiv Detail & Related papers (2023-12-17T09:40:36Z) - Reduce, Reuse, Recycle: Building Greener Sustainable Software [0.22252684361733285]
Data centers account for more than one percent of all power usage worldwide.
Non-trivial energy savings can be achieved in software by making energy-conscious decisions regarding basic aspects of programming.
arXiv Detail & Related papers (2023-11-03T03:03:13Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Data-driven quantitative analysis of an integrated open digital
ecosystems platform for user-centric energy retrofits: A case study in
Northern Sweden [0.0]
We present an open digital ecosystem based on web-framework with a functional back-end server in user-centric energy retrofits.
Data-driven web framework is proposed for building energy renovation benchmarking.
arXiv Detail & Related papers (2023-09-21T08:05:10Z) - ENCOVIZ: An open-source, secure and multi-role energy consumption
visualisation platform [1.181393338951936]
We present the ENCOVIZ platform, a multi-role, secure, energy consumption visualization platform with built-in analytics.
ENCOVIZ has been built in accordance with the best visualisation practices, on top of open source technologies.
arXiv Detail & Related papers (2023-05-09T09:48:09Z) - Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A
Multi-Agent Reinforcement Learning Approach [48.18355658448509]
Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption.
Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy.
We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
arXiv Detail & Related papers (2023-04-17T02:12:30Z) - Learning, Computing, and Trustworthiness in Intelligent IoT
Environments: Performance-Energy Tradeoffs [62.91362897985057]
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications.
This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
arXiv Detail & Related papers (2021-10-04T19:41:42Z) - Power Modeling for Effective Datacenter Planning and Compute Management [53.41102502425513]
We discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads.
We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features.
arXiv Detail & Related papers (2021-03-22T21:22:51Z) - Knowledge Integration of Collaborative Product Design Using Cloud
Computing Infrastructure [65.2157099438235]
The main focus of this paper is the concept of ongoing research in providing the knowledge integration service for collaborative product design and development using cloud computing infrastructure.
Proposed knowledge integration services support users by giving real-time access to knowledge resources.
arXiv Detail & Related papers (2020-01-16T18:44:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.