The Arctic Green Computing group is looking for Masters students to work on Capstone and Masters projects in energy informatics (e.g., energy-efficient computing). Please contact us if you are interested in the projects and are a Masters student at the Department of Computer Science, UiT
Below are some of the available topics:
Three Research Assistants (20% positions) within the pre-project VEDA moVE the DAta to balance the grid
The IFI Department at UiT is looking for 3 Research Assistants to work on preliminary research tasks within the pre-project VEDA “moVE the DAta to balance the grid”.
The pre-project received strategic funds from the ARC (Arctic Centre for Sustainable Energy) to reach preliminary subgoals that would lead to a full concept development by 2021.
The project
The key research question is: how can we optimally schedule jobs between different datacentres (that may be located in different geographical zones and in different time zones), to balance the local grid?
Mathematical optimisation models and energy-aware runtime systems can be developed, in order to study the effects of datacentres flexibility within power systems and energy systems design, expansion and operations.
The pre-project is coordinated at the IFI Department of UiT, but it has links with other departments at UiT that are part of the ARC, in particular social science, mathematics and physics.
The subgoals and tasks
· Put together a literature review that investigates the state-of-the art of mathematical optimisation and energy-aware runtime systems, applied to datacentres flexibility within power and energy systems.
· Identify potential industries that may be interested in being involved in the VEDA initiative
· Further develop the overall concept in a way suitable for the submission of a full proposal within the Norwegian Research Council calls that will appear in 2021.
· Develop and test first basic prototypes:
- Multihorizon stochastic optimisation models to address investment and operational optimisation on a power systems perspective, by considering flexibility from datacentres in different zones
- Energy-aware runtime systems for jobs management within a datacentre and across datacentres
The ideal profiles for Research Assistants
- Master level in Computer Science, or Applied Mathematics, or Power Systems Engineering, or related subjects.
- Willingness to acquire new knowledge in fields and subjects that are not yet part of your background.
- Willingness to work using an interdisciplinary approach that includes the main disciplines of Energy and Power Systems, Operations Research, Mathematical Modelling and Optimisation, Decisions Support Systems, Green computing and Distributed Systems.
Contacts at IFI
- Assoc. Prof. Chiara Bordin chiara.bordin@uit.no
- Prof. Phuong Ha phuong.hoai.ha@uit.no
- Prof. Alexander Horsch alexander.horsch@uit.no
Optimizing the trade-off between energy efficiency and accuracy in approximate computing
Energy consumption is becoming a major concern across the full spectrum of computing systems from mobile devices (i.e., battery life) to data centers (i.e. energy cost). Energy consumption can be reduced using approximate computing that allows to trade accuracy for energy efficiency. Approximate computing is applicable to several application domains such as big data applications.
This project will investigate the trade-off between energy efficiency and accuracy in approximate computing and subsequently optimize it. The trade-off will be investigated and optimized at different levels (e.g., application and system) and with different objectives (i.e. minimizing energy consumption or providing guarantees on energy consumption).
Low-power continuous 3D face authentication for mobile devices
Face authentication, which leverages built-in cameras on mobile devices, provides a memory-less alternative to legacy passwords. The 3D face authentication is one of the liveness detection techniques for face authentication to defend against the media-based facial forgery (MFF) where the attacker forges a photo/video containing the victim’s face. The 3D face authentication detects the depth characteristics of a real face using optical flow analysis and changes of face views. Due to its optical flow analysis, the continuous 3D face authentication consumes a lot of energy for mobile devices, limiting its deployment in mobile applications.
This project will investigate and develop low-power continuous 3D face authentication, making the authentication more viable for mobile applications. The new low-power continuous 3D face authentication will utilize the ultra low-power machine vision technology developed by Movidius for mobile devices. The new authentication will be developed on Movidius Myriad2 platform.
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