PhD Candidate in Energy Efficiency Analysis of Computer Systems Applications

The computing continuum, from devices to the cloud, is relying on digital services. In this project you will focus on measuring the energy consumption of the applications and computing systems that compose these digital services, and translate the results into energy labels. Scientific discovery, product development, data science and artificial intelligence, online shopping, and entertainment rely increasingly on digital services. Digital services come with a significant, rapidly-increasing energy cost, raising sustainability concerns. Even more concerning, worldwide estimates project the ICT sector to reach 21% of the global energy consumption by 2030.

Users interacting with devices — mobile phones, tablets, or laptops — trigger entire digital chains, combining multiple communicating computing layers and data transfers: from the device itself, through the edge, to the datacenter. Each layer has its own computing infrastructure. At each layer, decisions are made about how, where and when applications are running and/or data are transferred. These decisions have a significant impact on the user-perceived quality-of-service (QoS), but also on the energy consumption – per layer, and for the entire digital chain. In this complex environment, even though the energy footprint of different devices along the chain might be known, the actual energy consumed by the application is unknown, because it depends on infrastructure choices, and on user QoS requirements, and on mapping decisions made on the edge and in the datacenter. In fact, the energy efficiency, i.e., the amount of energy consumed to perform the actual task at hand, is largely unknown for most digital chains.

The first step to reduce waste in computing is to quantify the energy efficiency of end-to-end digitalchains. This project will design an integrated framework (i.e., the methods, metrics, and tools) for this quantification effort. Specifically, we aim to define a reference architecture of digital chains, use it to define an analytical digital-chain energy-efficiency model that exposes the factors that impact energy efficiency along the chain, and support it with a high-level functional simulator to assess different operational scenarios and parameters that affect the energy efficiency of digital chains.

What are you going to do

• Perform high quality research in the field of computer science and/or engineering. Specifically, we foresee the following tasks:
  • select (and implement/tune) several candidate case-studies (of digital chains) to be used as real-life examples;
  • define and implement the tools and methods for accurate performance and energy measurement for different digital services along the chain, from device to datacenter; validate these methods and tools on the selected case-studies;
  • define a reference architecture of digital chains; validate the applicability of the reference architecture through on the selected case-studies;
  • implement basic models (using the reference architecture) to reason about the performance and energy of the entire chains of digital services; validating these models on the case-studies;
  • design and implement methods and tools to monitor the energy consumption of digital chains for digital services;
  • publish the results of the research in international venues (conferences and/or journals) in computer science and/or engineering;
  • combine all the research work in a PhD thesis.
• Work independently but in close collaboration with other members of the team, including the industrial partners.
• Take part in scientific and social activities with other members of the ZeroWaste lab, the research departments and the faculty.
• Contribute to dissemination activities about the project in national and international venues.
• Actively contribute to the teaching of the new Science for Design Bachelor program and relevant programs.