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SUMMARY:Towards Energy-Efficient Computing: Adaptive Management Strategies
  from Software to Systems
DTSTART:20150701T161500
DTEND:20150701T171500
DTSTAMP:20260407T134012Z
UID:4fa6a429a845dc71272f8d12eca9aa038aa40e35faec447ff5fa2a2f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Bio: Ayse K. Coskun is an associate professor in the Electrica
 l and Computer Engineering Department at Boston University. She received h
 er MS and PhD degrees in Computer Science and Engineering from University 
 of California\, San Diego. Coskun’s research interests are energy-effici
 ent computing\, 3D-stacked architectures\, embedded systems\, and intellig
 ent management of data centers. Prof. Coskun worked at Sun Microsystems (n
 ow Oracle)\, San Diego prior to her current position at BU. Coskun is a re
 cipient of the NSF CAREER award. She currently serves as an associate edit
 or for IEEE Embedded Systems Letters and writes a bi-monthly column on gre
 en computing at the Circuit Cellar magazine.\nEnergy efficiency is a centr
 al issue in all computing domains. In data centers\, operational and cooli
 ng costs impose significant sustainability challenges. In tandem\, computi
 ng systems run increasingly complex\, highly performance demanding workloa
 ds\, making the existing energy management policies inadequate. High power
  densities also increase the chip temperatures and thermal variations\, wh
 ich degrade system reliability\, add to the system design complexity\, and
  increase monetary cost of cooling.\nAchieving the target exascale computi
 ng performance\, and also\, designing a sustainable cloud computing future
  require the design of dynamic and intelligent techniques that recognize t
 he hardware-software characteristics and optimize the interplay among perf
 ormance\, energy\, and temperature in an application-aware manner. This ta
 lk discusses how to design adaptive workload management and power modulati
 on methods at server and at data center scale to help achieve a sustainabl
 e computing future. The talk includes techniques for integrating data cent
 ers into the emerging smart grid programs and touches upon some of the key
  data analytics challenges in computing systems.
LOCATION:INF 328 http://plan.epfl.ch/?room=INF%20328
STATUS:CONFIRMED
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