Green Computing Algorithmics

Kirk PruhsComputer Science Department

University of PittsburghPittsburgh, USA

Email: kirk@cs.pitt.edu

Abstract— The converging trends of society’s desire/need formore sustainable technologies, exponentially increasing power den-sities within computing devices, and exponentially more computingdevices, have inevitably pushed power and energy managementinto the forefront of computing design and management for purelyeconomic reasons. Thus we are in the midst of a green comput-ing revolution involving the redesign of information technologyhardware and software at all levels of the information technologystack. This revolution has spawned a multitude of technologicalchallenges, many of which are algorithmic in nature. We providepointers into the literature on the green computing algorithmics.

“What matters most to the computer designers at Google isnot speed, but power, low power, because data centers canconsume as much electricity as a city.” — Google’s CEOEric Schmidt [7].

1. INTRODUCTION

Energy-efficient computing and power management aretwo important focus areas within green computing, andgreen computing is in turn part of society’s desire/need forsustainable technologies [8]. Further, the following converg-ing trends in Information Technology (IT) have inevitablypushed power and energy management into the forefront ofIT design and management for purely economic reasons:

• An exponential growth in the number of IT devices,particularly mobile IT devices

• As a corollary of Moore’s law, an exponential growthin the power density, and total power, of IT devices

• Only a linear growth in energy density of batteriesConsequences of the convergence of these trends include:

• The energy cost for cooling chips with traditionalarchitectures became prohibitively expensive.

• The dominant cost of operation for large data centersbecame energy costs.

• The fraction of society’s total energy usage that isdue to IT technology became significant (the mostcommonly cited figure is a few percent of the total,comparable to the airline industry).

• Battery life became a limiting factor in the usefulnessof mobile IT devices

The rapid emergence of power and energy as first orderresources has spurred revolutionary changes in the designand management of IT at all levels of the IT stack. Some

examples of technological IT changes motivated by the needfor more energy and power efficient technologies include:

• Intel and AMD’s switch from single processor archi-tectures to multiprocessor architectures was motivatedby the prohibitive expense of cooling ever hotter singleprocessor chips. Due to the convex nature of the speedto power relationship on processors, multiple lowerspeed processors with aggregate speed s consume muchless power than one processor with speed s.

• The replacement of traditional disks by Solid StateDrives/Memory in mobile devices such as the newMacBook Air is primarily motivated by the fact thatsolid state memory consumes less power.

• AMD and Intel’s introduction of speed scalable proces-sors, and associated software (e.g. AMD’s PowerNow,AMD’s Cool’n’Quiet, and Intel’s SpeedStep) to managethe speed and power of the processors.

• Networking device makers, such as Cisco, have re-designed their routers to be more than twice as energyefficient as a decade ago. Further, these networkingcompanies produce software solutions to aid in the de-sign and management of more energy efficient networksand feature the energy efficiency of their technologiesheavily in their marketing.

• Operators of large data centers, such as Google, havecompletely redesigned their data centers, dropping thePUE’s from above 2 to below 1.2 (meaning that morethan 80% of the incoming energy now reaches thecomputing equipment).

This green computing revolution has spawned a multitudeof technological challenges, many of which are algorithmicin nature. The most obvious type of algorithmic probleminvolves directly managing power, energy or temperatureas a resource. Perhaps less obviously, the green computingrevolution gives rise to algorithmic problems that don’tdirectly involve managing power or energy. Instead theseproblems arise because the new technology, which wasadopted because of energy and power considerations, hasdifferent physical properties than the incumbent technolo-gies. And these new properties necessitate the change of theobjectives of the operating system when it is managing theseresources, thus leading to new algorithmic problems.

2011 52nd Annual IEEE Symposium on Foundations of Computer Science

0272-5428/11 $26.00 © 2011 IEEE

DOI

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2011 52nd Annual IEEE Symposium on Foundations of Computer Science

0272-5428/11 $26.00 © 2011 IEEE

DOI 10.1109/FOCS.2011.44

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2011 52nd Annual IEEE Symposium on Foundations of Computer Science

0272-5428/11 $26.00 © 2011 IEEE

DOI 10.1109/FOCS.2011.44

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2011 IEEE 52nd Annual Symposium on Foundations of Computer Science

0272-5428/11 $26.00 © 2011 IEEE

DOI 10.1109/FOCS.2011.44

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For those wishing to understand the current state ofgreen computing algorithmic research, let me recommendsome references. A recent CACM article explaining greencomputing to a general computing audience can be foundin [4]. An introduction to green computing algorithmics canbe found in [6]; This article is now in many ways quitedated, but it is a good starting point that explains many ofthe basics. A more up to date survey of the literature canbe found in [3], and an earlier version can be found in [2].An introduction to the potential function methods used toreason about energy in many of the papers can be found in[5]. Many papers from the algorithmic literature, and fromrecent systems conferences, can be found on the web pageof my science of power management course [1].

Looking towards the future, I believe it would likely bebeneficial to build a science of algorithmic power man-agement to support the computing community’s ability toabstractly reason about power, energy and temperature. Theascension of power, to join time and space, to the top ranksof scarce computational resources is relatively recent. Overthe last several decades, when time and space were thekey computational resources, computer science researchersdeveloped many techniques for designing time and spaceefficient algorithms, and for analyzing the time and spacerequired by particular algorithms on simple models of acomputer. These techniques are commonly taught in al-gorithms classes that are required for computer scientists.While one may criticize the RAM model as being overlysimplistic, or criticize big-Oh notation as too crude, theability to reason abstractly about time and space in a simplecomputational model is undoubtedly a valuable skill forsoftware engineers. It is fair to say that computer scientists

are generally less adept at reasoning about power, energy,and temperature than at reasoning about time and space.It is my expectation that a science of power management,of which algorithmics will play a significant part, will betaught to future software engineers. This science will servethese software engineers, when faced with problems inwhich power, energy, and/or temperature are the key scarceresources, just as the science of algorithms serves them whenthey are concerned with the resources of time and space.

ACKNOWLEDGMENT

Supported in part by NSF grant CCF-0830558 and anIBM Faculty Award. I want to thank my many collaboratorswho have made research on green computing algorithmicsso rewarding.

REFERENCES

[1] http://www.cs.pitt.edu/∼kirk/cs3150spring2010/.[2] S. Albers, “Algorithms for energy saving,” in Efficient Algo-

rithms, 2009, pp. 173–186.[3] ——, “Energy-efficient algorithms,” Communications of the

ACM, vol. 53, no. 5, pp. 86–96, 2010.

[4] G. Goth, “Chipping away at greenhouse gases,” Communica-tions of the ACM, vol. 54, pp. 13–15, February 2011.

[5] S. Im, B. Moseley, and K. Pruhs, “A tutorial on amortized localcompetitiveness in online scheduling,” SIGACT News, vol. 42,no. 2, pp. 83–97, 2011.

[6] S. Irani and K. Pruhs, “Algorithmic problems in power man-agement,” SIGACT News, vol. 36, no. 2, pp. 63–76, 2005.

[7] J. Markoff and S. Lohr, “Intel’s huge bet turns iffy,” New YorkTimes, September 29 2002.

[8] S. Murugesan, “Harnessing green it: Principles and practices,”IEEE IT Professional, pp. 24–33, January 2008.

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