Energy-efficient Scheduling in the Non-clairvoyant Model

A fundamental problem in energy-efficient computing is to schedule multiple jobs released over time on a single machine with adjustable speed so as to minimize the sum of flow-time (delay) and energy. Note that the two objectives are in conflict: higher speeds reduce flow-time at the cost of increased energy consumption. In this talk, motivated by datacenter applications, I will consider the non-clairvoyant version of this problem where the density (importance) of a job is known when the job arrives but its volume (processing length) is known only after the job has been completely processed. Using a novel technique called incremental analysis, we give a constant-competitive algorithm for this problem, which is the first non-trivial result for the non-clairvoyant setting. (Based on joint work with Yossi Azar, Nikhil Devanur, and Zhiyi Huang, recipient of the “Best paper” award in SPAA 2015.)

Speaker Details

Debmalya Panigrahi is an Assistant Professor of Computer Science at Duke University. He is interested in the design and analysis of efficient algorithms for combinatorial optimization and Internet applications. He received his PhD from MIT in 2012, where he was an MIT Presidential Fellow.

Series:
Microsoft Research Talks
Date:
Speakers:
Debmalya Panigrahi
Affiliation:
Duke University

    • Portrait of Debmalya Panigrahi

      Debmalya Panigrahi

    • Portrait of Jeff Running

      Jeff Running

Series: Microsoft Research Talks