Pooja Rani

Pooja Rani

Postdoctoral Researcher

Impact of energy patterns in web applications

Seminar topic, University of Zurich, 2023

Context:

The intensive use of software in many aspects of industries is becoming more and more common. Including other factors impacting climate, the technology sector also plays a role. Therefore, in recent years, software engineering research has attempted to comprehend the energy consumption and implication of the software development process by building various tools and frameworks[1]. However, not all developers are aware of sustainable software, or energy-efficient practices; therefore, energy-related anti-patterns are more common than expected, and eventually, developing green software is still challenging.

Problem:

This study aims at assessing the impact of various energy-related patterns proposed by Cruz et al. Cruz et al. developed a catalog of 22 design patterns related to the energy efficiency of mobile apps. Some patterns are applicable in Web development as well. We find the anti-patterns that consume more energy than others. We assess their impact on selected open-source projects[2][3]. We also design a pilot study to understand the strategies that can be adopted to avoid them.

Online

Steps:

  • Study the 22 energy patterns.
  • Identify the tools (e.g., Intel’s Running Average Power Limit (RAPL) interface) to measure the impact of the patterns in open-source web applications[4].
  • Identify the patterns that consume more energy.
  • Explore the strategies that can be used to avoid the anti-patterns.
  1. Pinto et al, Mining Questions about Software Energy Consumption, 2014, https://dl.acm.org/doi/pdf/10.1145/2597073.2597110
  2. Tahir et al., A large scale study on how developers discuss code smells and anti-patterns in Stack Exchange sites, 2020, https://www.sciencedirect.com/science/article/pii/S0950584920300926
  3. Procaccianti et al. 2016, “Empirical evaluation of two best practices for energy-efficient software development” https://doi.org/10.1016/j.jss.2016.02.035
  4. Shanbhag et al. “Onthe Energy Consumption of Different Dataframe Processing Libraries- An Exploratory Study”, https://arxiv.org/pdf/2209.05258.pdf