A Manifesto for Energy-Aware Software

Fonseca, Alcides; Kazman, Rick; Lago, Patricia

doi:10.1109/ms.2019.2924498

Cited by 37 publications

(20 citation statements)

References 5 publications

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“…Figure 5 shows an estimated growth in internet data traffic, power consumption & workload of data centers between 2015 to 2021. Internet traffic in 2015 stood at 100 exabyte per month (Eb/M) and it is projected to be reach 519 Eb/M at compound annual growth rate of 31.58% [98]. Increasing growth of internet traffic, computational requirement, data storage and communication networks have fueled emissions in the environment.…”

Section: Environmental Hazardsmentioning

confidence: 99%

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

et al. 2021

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Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.INDEX TERMS Fifth generation, Internet of Things, green Internet of Things, fog, machine learning.

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Section: Environmental Hazardsmentioning

confidence: 99%

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

et al. 2021

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“…Meeting such diverse requirements has also resulted in increased ICT energy consumption. By 2025, the ICT industry itself could be responsible for 30% of power consumption globally, where data centers alone account for 3% of carbon emission [2]. In mobile networks, 80% of the total energy of cellular networks is consumed by base stations and has pivotal importance for energy efficiency improvements [3].…”

Section: Introductionmentioning

confidence: 99%

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

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“…Ideally, the impact label of an offered service would be something as easy to communicate as the energy ranking of commercial devices, allowing for easy comparison between them by the users, and resulting in raising awareness across the board [10]. However, achieving this goal is far from trivial.…”

Section: Environmental Dimensionmentioning

confidence: 99%

Software Sustainability in the Age of Everything as a Service

Andrikopoulos

Lago

2021

Lecture Notes in Computer Science

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A Manifesto for Energy-Aware Software

Cited by 37 publications

References 5 publications

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Software Sustainability in the Age of Everything as a Service

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