Energy-Efficient Dynamic Provisioning in Data Centers: The Benefit of Seeing the Future

Speaker:        Dr. Minghua Chen
                Department of Information Engineering
                The Chinese University of Hong Kong

Title:          "Energy-Efficient Dynamic Provisioning in Data Centers:
                The Benefit of Seeing the Future"

Date:           Monday, 30 March 2015

Time:           4:00pm - 5:00pm

Venue:          Lecture Theatre F (near lifts 25/26), HKUST

Abstract:

Energy consumption represents a significant cost in data center operation.
In 2010, data centers world-wide consumed 240 billion kWh electricity
(1.3% of the world total), enough to power 5+ Hong Kong or roughly the
entire Spain. However, real-world statistics reveals that a large fraction
of the energy is used to power idle servers when the workload is low.
Dynamic provisioning techniques aim at saving this portion of the energy,
by turning off unnecessary servers. In dynamic provisioning, it is a
common approach to predict future workload to certain extent and exploit
the information to achieve good performance. This naturally leads to the
following fundamental questions:

- Can we design solutions that require zero future workload information,
called online solutions, yet still achieve close-to-optimal performance?

- Can we characterize the benefit of knowing future workload information
in dynamic provisioning?

In this work, we seek answers to the above questions. In particular, we
develop online dynamic provisioning solutions with and without future
workload information available. We first reveal an elegant structure of
the off-line dynamic provisioning problem, which allows us to characterize
the optimal solution in a "divide-and-conquer" manner. We then exploit
this insight to design two online algorithms with competitive ratios and ,
respectively, where is the normalized size of a look-ahead window in which
exact workload prediction is available. We prove that these competitive
ratios are the best possible for deterministic and randomized algorithms;
hence, they characterize the benefit of predicting future workload. A
fundamental observation is that future workload information beyond the
full-size look-ahead window (corresponding to ) will not improve dynamic
provisioning performance. We remark that our results hold as long as the
overall energy demands (including mainly server, cooling, and power
conditioning) is a convex and increasing function in the total number of
active servers. Our algorithms are decentralized and easy to implement. We
demonstrate 20-71% of energy saving in a case study using real-world
traces.

More information can be found at
http://www.ie.cuhk.edu.hk/~mhchen/projects/dynamic.provisioning.in.data.centers.html


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Biography:

Minghua Chen received his B.Eng. and M.S. degrees from the Department of
Electronic Engineering at Tsinghua University in 1999 and 2001,
respectively. He received his Ph.D. degree from the Department of
Electrical Engineering and Computer Sciences at University of California
at Berkeley in 2006. He spent one year visiting Microsoft Research Redmond
as a Postdoc Researcher. He joined the Department of Information
Engineering, the Chinese University of Hong Kong, in 2007, where he
currently is an Associate Professor. He is also an Adjunct Associate
Professor in Peking University Shenzhen Graduate School in 2011-2014. He
received the Eli Jury award from UC Berkeley in 2007 (presented to a
graduate student or recent alumnus for outstanding achievement in the area
of Systems, Communications, Control, or Signal Processing) and The Chinese
University of Hong Kong Young Researcher Award in 2013. He also received
several best paper awards, including the IEEE ICME Best Paper Award in
2009, the IEEE Transactions on Multimedia Prize Paper Award in 2009, and
the ACM Multimedia Best Paper Award in 2012. He is currently an Associate
Editor of the IEEE/ACM Transactions on Networking. His recent research
interests include energy systems (e.g., microgrids and energy-efficient
data centers), distributed optimization, multimedia networking, wireless
networking, network coding, and secure network communications.