Project Description
Determining the Influence of Software Configurations on Energy Consumption (funded by the DFG:
590,000€, 2018-2021)
Reducing energy consumption is an increasingly important goal of our society. It is predicted that
IT energy usage will almost double by the year 2020, not only due to numerous data centers, but also
because of billions of end devices. At the same time, software and hardware systems grow ever more
complex by providing configuration capabilities, which makes it difficult to select a software and
hardware system that not only satisfies the functional requirements, but also considers its energy
consumption. In general, there are two important causes of energy wasting of IT systems: sub-optimal
configuration of configurable systems (users' perspective) and inefficient implementation
(developers' perspective).
Green Configuration will target both causes to reduce the energy consumption of IT systems. In this
project, we will develop techniques to measure, predict, and optimize the influence of features and
their interactions on the energy consumption of a configurable system. Furthermore, we will develop
methods and analysis techniques to support developers in identifying the cause of energy problems in
the source code.
The heart of the project is the energy-influence model, which captures the influence of software,
hardware, and workload features on energy consumption. We will determine the influence using a
combination of black-box and white-box measurements with modern learning strategies (e.g., active
learning and transfer learning). We will use tailored sampling techniques and related them to the
results of control-flow and data-flow analysis techniques to determine optimal configurations and
trace the origin of energy leaks. The goal is to learn an accurate influence model with few
measurements, which can then be use to relate the individual influences to regions of the source
code.
The influence model builds the foundation of the Green Configurator, a tool to show the consequences
of configuration decisions to users. The Green Analyzer builds also on the influence model to
support developers in maintenance tasks, for example, by highlighting regions in the source code
with high energy consumption.
Green Configuration commits to the German goal of reducing energy consumption. By making
consequences of configuration decisions visible to the user, we aim at inducing long-term behavioral
changes that potentially save more energy than pure software-based optimizations. The foundational
character of the project can fertilize further research in related fields. For instance, we
investigate the issue of the effect of software-hardware configuration on quality attributes and
provide accurate and realistic surrogate models for multi-objective optimization. By combining
software analysis with machine learning, we also expect new insights about the effect of program
flow and control flow on energy consumption.
