Self-adaptation approaches for energy efficiency

Abstract

The increasing energy demands of software systems have set an essential software quality requirement: energy efficiency. At the same time, the many contextual changes faced by software systems during execution can hamper their functionality and overall quality. To address both problems, self-adaptation approaches can empower software systems, at both design-time and runtime, to adapt to dynamic conditions. In this way, software systems can be more resilient to failure, hence more trustful to satisfy the demands of modern digital society. In this paper, we perform a systematic literature review to study the state-of-the-art on existing self-adaptation approaches for energy efficiency. We analyze the identified approaches from three different perspectives, namely publication trends, application domains, and types of software systems. Our findings can help solution providers to make guided decisions to enable self-adaptability in designing and engineering software systems. CCS CONCEPTS • Software and its engineering → Designing software; Software design engineering;

Mining Energy-Related Practices in Robotics Software

“…The high discard-rate in this step is not surprising, but is rather in accordance with existing research confirming that developers tend to have limited knowledge of energy efficiency [37]. Mobile Issues Swanborn and Malavolta [50] Robotics Literature Cruz and Abreu [16] Mobile Commits, Issues, PRs Moghaddam, Lago, and Ban [35] Generic Literature Matalonga et al [33] Mobile System Events Chowdhury and Hindle [13] Generic Commit Messages Bavota [9] Generic Commit Messages Moura et al [36] Generic Commits Malik, Zhao and Godfrey [32] Generic Stack Overflow Procaccianti, Lago and Bevini [44] Cloud Literature Pinto, Castor and Liu [42] Generic Stack Overflow Procaccianti, Bevini and Lago [43] Cloud Literature Calero, Bertoa and Moraga [10] Generic Literature Pathak, Hu and Zhang [38] Mobile Issues, Forum Posts Phase 2 -In this phase we manually analyze all the 5,111 data points for removing false positives. This phase is performed systematically and iteratively by three researchers.…”

“…In this phase, it is fundamental to identify the search terms that best fit with the problem at hand [9]. Two researchers identify the search terms via a semisystematic process where they: (i) collect a set of 14 scientific publications containing a keyword-based search applied in the context of green software across several domains and different types of targeted data (see Table I), (ii) extract all the search terms used in each publication, (iii) merge and adapt the extracted search terms according to the robotics domain (e.g., we merge the "energy efficiency" term used in [35] since it is dominated by the "energy" term used in [42]). Finally, we query our MongoDB database by considering the list of energy-related search terms.…”

EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems

“…4) Planner: The Planner component strategizes adaptations based on insights from the Analyzer, specifically addressing energy efficiency and confidence score through model selection under two scenarios as explained in Algorithm 1. The algorithm navigates decision-making under two primary scenarios-high energy and low confidence-leveraging a unified score metric for model selection (lines [11][12][13][14][15][16]. The score (score m[i]), calculated for each model, combines energy efficiency and confidence inversely (line 10), guiding the selection process.…”

“…The concept of self-adaptation in software, originating from IBM's autonomic computing [21], has evolved to include Machine Learning Systems (MLS). Research in this area [14], [22], [23] categorizes self-adaptation into software design approaches (SDA) and system engineering approaches (SEA), focusing on designtime solutions. Recent studies [15], [24] explore enhancing adaptability at runtime, including unsupervised learning and model switching.…”

“…This gap highlights the need for strategies that reduce energy consumption without compromising performance and can adjust to varying operational demands. The potential of self-adaptation techniques, which balance energy efficiency with QoS, remains largely unexplored in this context [12]. As the ICT sector's energy consumption is expected to increase, creating adaptive, energy-efficient MLS is paramount [13].…”