{"id":2991,"date":"2022-02-22T17:55:18","date_gmt":"2022-02-22T16:55:18","guid":{"rendered":"https:\/\/i4wpdev.cs.fau.de\/?page_id=2991"},"modified":"2026-02-03T10:19:21","modified_gmt":"2026-02-03T09:19:21","slug":"e3","status":"publish","type":"page","link":"https:\/\/i4wpdev.cs.fau.de\/en\/research\/e3","title":{"rendered":"E\u00b3"},"content":{"rendered":"

Energy-aware Execution Environments<\/h3>

(Own Funds)<\/p>


Project leader: <\/strong>Tobias Distler<\/a><\/span>
Project members: <\/strong>Laura Lawniczak<\/a><\/span>, Christopher Eibel<\/a><\/span>
Start date: <\/strong>1. January 2018
End date: <\/strong>31. December 2026
Acronym: <\/strong>E\u00b3<\/p>

Abstract: <\/h4>

The processing of large amounts of data on distributed execution platforms such as MapReduce or Heron contributes significantly to the energy consumption of today's data centers. The E\u00b3 project aims at minimizing the power consumption of such execution environments without sacrificing performance. For this purpose, the project develops means to make execution environments and data-processing platforms energy aware and to enable them to exploit knowledge about applications to dynamically adapt the power consumption of the underlying hardware. To measure and control the energy consumption of hardware units, E\u00b3's energy-aware platforms rely on hardware features provided by modern processors that allow the system software of a server to regulate the server's power usage at runtime by enforcing configurable upper limits. As a key benefit, this approach makes it possible to build data-processing and execution platforms that, on the one hand, save energy during phases in which only low and medium workloads need to be handled and, on the other hand, are still able to offer full processing power during periods of high workloads.<\/p><\/p>

Publications: <\/h4>