Green Configuration: Can Artificial Intelligence Help Reduce Energy Consumption of Configurable Software Systems?

Siegmund, Norbert; Dorn, Johannes; Weber, Max; Kaltenecker, Christian; Apel, Sven

doi:10.1109/mc.2021.3120048

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The authors provide 36 benchmarks, considering different tasks, goals, domain of application and state of their retrieved data. Siegmund et al [23] focus on techniques for reducing energy consumption using AI in software systems. As a significant amount of energy wasted for computation can be saved by optimizing the choice of the parameters, the authors suggest AI and ML methods for finding more efficient configurations.…”

Section: Structural Matricesmentioning

confidence: 99%

Toward Green AI: A Methodological Survey of the Scientific Literature

Barbierato,

Gatti

2024

IEEE Access

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The pervasive deployment of Deep Learning models has recently prompted apprehensions regarding their ecological footprint, owing to the exorbitant levels of energy consumption necessitated by the training and inference processes. The term "Red AI" is employed to denote artificial intelligence (AI) models that undergo training using resource-intensive methodologies on very large datasets. This practice can engender substantial energy usage and emissions of carbon, thereby opposing "Green AI." The latter concept alludes to AI models designed for similar efficiency and reduced environmental impact. This objective is realized through the utilization of smaller datasets, less computationally intensive training techniques, or sustainable energy resources. While Red AI prioritizes accuracy and performance, Green AI emphasizes efficiency and sustainability. Given that both paradigms exhibit advantages and limitations, the debates around the topics have burgeoned in the scientific arena, delving into novel algorithms, hardware innovations, and improved data utilization techniques aimed at mitigating the ecological consequences of intricate applications such as GPT and BERT. Nevertheless, due to the relative novelty of this debate, not much effort has been dedicated yet to contextualizing the essence of Red AI and the prospects of Green AI in a coherent framework. Within this context, the present work contributes by meticulously delineating both domains through a multifaceted analysis of their causes and ramifications, described from the points of computer architectures, data structures, and algorithms. Additionally, the study reviews notable instances of study cases based on complex Red AI models. The primary contribution of this article encompasses a comprehensive survey of Red and Green AI, stemming from a selection of the literature performed by the authors, subsequently organized into distinct clusters. These clusters encompass i) articles that qualitatively or quantitatively address the issue of Red AI, identifying Green AI as a plausible remedy, ii) articles offering insights into the environmental impact associated with the deployment of extensive Deep Learning models, and iii) articles introducing the techniques underpinning Green AI, aiming at mitigating the cost of Red AI. The outcome emerging from the analysis performed by this work consists of a compromise between sustainability in contrast to the performance of AI tools. Unless the complex training and inference procedures of software models mitigate their environmental impact, it will be necessary to decrease the level of accuracy of production systems, inevitably conflicting with the objective of the major AI vendors. The outcomes of this work would be beneficial to scholars pursuing intricate Deep Learning architectures in scientific research, as well as AI enterprises struggling with the protracted training demands of commercial products within the realms of Computer Vision and Natural Language Processing.INDEX TERMS green ai, red ai, survey, environmenta...

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Section: Structural Matricesmentioning

confidence: 99%

Toward Green AI: A Methodological Survey of the Scientific Literature

Barbierato,

Gatti

2024

IEEE Access

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“…Such a graceful combination is required to address complex challenges such as predicting the performance or energy consumption of variability-intensive systems [17]. We think now is the time to do it, as prompt-enabled language models generate code and documentation for features automatically [5].…”

Section: Explicit or Implicit?mentioning

confidence: 99%

Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems

Temple

Perrouin

2023

Proceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems

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Software variability engineering benefits from Machine Learning (ML) to learn e.g., variability-aware performance models, explore variants of interest and minimize their energy impact. As the number of applications of combining variability with ML grows, we would like to reflect on what is the core to the configuration process in software variability and inference in ML: feature engineering. These disciplines previously managed features explicitly, easing graceful combinations. Now, deep learning techniques derive automatically obscure but efficient features from data. Shall we give up explicit feature management in variability-intensive systems to embrace machine learning advances?

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“…In [29] we proved that a priori there is no single code that is optimal in terms of energy efficiency, but this depends on the hardware it is executed on, as well as channel conditions and application requirements. Given the recently increasing interest in energy-aware systems [60,61], a further line of research would involve making DeepSHARQ energy-aware as well. This involves changing the search problem to incorporate energy as a) an input metric, i.e., an application-defined energy limit per application packet, and b) an output metric, i.e., how much energy a coding configuration demands.…”

Section: Future Workmentioning

confidence: 99%

DeepSHARQ: Hybrid Error Coding using Deep Learning

Pereira¹,

Vogelgesang²,

Miodek³

et al. 2023

Preprint

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Purpose: Cyber-Physical Systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactiveschemes cannot always fulfill application demands or have suboptimal performance. However, findingoptimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—underchanging channel conditions is a computationally challenging task. Methods: Recently, the systems community has started addressing these sorts of problems usinghybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts ofthe problem and learning-based approaches for parts that must be estimated (either for reasons ofuncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work andlimit the learning problem to block length prediction, the major contributor to inference time (andits variation) when searching for hybrid error coding configurations. The remaining parameters arefound algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. Results: DeepSHARQ applies block length regularization in order to reduce the neural networksin comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerningthe channel efficiency of coding configurations it generates, as it is trained so deviations from theoptimum are upper-bound by a configurable percentage. Additionally, DeepSHARQ is capable ofreacting to channel changes in real-time, thereby enabling Cyber-Physical Systems even on resource-constrained platforms. Conclusion: Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ toreact to channel changes faster and with a more predictable time than solutions that rely only oneither of the two approaches.

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Green Configuration: Can Artificial Intelligence Help Reduce Energy Consumption of Configurable Software Systems?

Cited by 8 publications

References 20 publications

Toward Green AI: A Methodological Survey of the Scientific Literature

Toward Green AI: A Methodological Survey of the Scientific Literature

Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems

DeepSHARQ: Hybrid Error Coding using Deep Learning

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