Parallel Distributed Infrastructure for Minimization of Energy

Ten minutes with…Pascal Felber, University of Neuchâtel

Tue, 2014-12-02

Pascal Felber

Pascal Felber leads the research group on Complex Systems in the Computer Science department at the University of Neuchâtel. The department focuses on large-scale distributed, concurrent and dependable systems. Along with Pascal, University of Neuchâtel researchers Anita Sobe, Yarco Hayduk and Mascha Kurpicz are also involved in the project.


Can you tell me a bit about your main research interests? What led you to work in this field?

My main research interests are distributed and concurrent systems. The same problems that you find in distributed systems are also encountered in concurrent systems; these include synchronisation problems, access to shared data, fault tolerance, or various kinds of agreement problems.  

After completing my PhD on fault-tolerant distributed computing, which was one of the topics of interest at EPFL (École Polytechnique Fédérale de Lausanne), the university where I studied, I went to work in in the United States and spent some time at Oracle and at Bell Labs. 


What, for you, are the most compelling reasons why we should create more energy-efficient computing systems?

The evolution of cloud computing with the development of huge data centres where you mutualise resources means that there is greater incentive to reduce the footprint.

Neuchâtel, with its long-established watchmaking industry, is a hub of microelectronics research. It has a history of energy research, partly due to the requirement for low energy consumption in microelectronics. It also specialises in renewable energy: for instance, it is where the first set of ‘white’ solar panels have been developed, and there is a whole research culture based on this field in Neuchâtel.


What, for you, are the key technical challenges which need to be tackled in order to achieve more energy-efficient computing systems?

First we need more efficient hardware, with more efficient chips; this needs to be combined with more intelligent software which can maximise the use of the hardware. Most of the energy loss in ICT currently comes from idle computers, so greater energy efficiency can be achieved by creating low-energy computers used 100% of the time.

Another aspect is making better use of computers: people often browse the web or watch videos on YouTube, for example, without realising how much energy this takes up. I think a cultural change is necessary. Even clicking on ‘search’ in Google generates much more activity globally in the system than users might expect.


Is it possible to deliver genuine energy savings while achieving optimum performance?

There is always a balance between performance and energy. If you want to increase performance slightly, you often have to greatly increase the energy. For me, it’s about making more reasonable use of resources and prioritisation, i.e., distributing resources by also taking into account what is critical and what is not.


What are the main improvements which you would like to see in the computing systems of the future?

I would like to see systems which are more intelligent in the way they operate – ones which adapt to ensure better energy consumption based on need. For example, I love embedded systems and would love to buy a smart watch, but a watch with a battery life of only one day is a no go. Smart products should operate at the level at which you need them, so a watch could be on standby all day without any of the ‘smart’ features enabled.


What will the lasting impact of the ParaDIME project be?

This is a great project for facilitating future research focusing on how to use resources more effectively. It has given us the opportunity to collaborate with researchers on concrete problems that need to be solved.

As for the wider impact, this project has shown that energy efficiency is not a simple problem, but rather one involving a lot of different aspects and parties, both at the level of hardware and software. There are many variables and many trade-offs to be investigated. The most likely solution will be to provide a mix of different hardware components and software systems that can be leveraged according to the problem to be solved.