Table Of ContentLecture Notes in Computer Science 7587
CommencedPublicationin1973
FoundingandFormerSeriesEditors:
GerhardGoos,JurisHartmanis,andJanvanLeeuwen
EditorialBoard
DavidHutchison
LancasterUniversity,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler
UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
AlfredKobsa
UniversityofCalifornia,Irvine,CA,USA
FriedemannMattern
ETHZurich,Switzerland
JohnC.Mitchell
StanfordUniversity,CA,USA
MoniNaor
WeizmannInstituteofScience,Rehovot,Israel
OscarNierstrasz
UniversityofBern,Switzerland
C.PanduRangan
IndianInstituteofTechnology,Madras,India
BernhardSteffen
TUDortmundUniversity,Germany
MadhuSudan
MicrosoftResearch,Cambridge,MA,USA
DemetriTerzopoulos
UniversityofCalifornia,LosAngeles,CA,USA
DougTygar
UniversityofCalifornia,Berkeley,CA,USA
GerhardWeikum
MaxPlanckInstituteforInformatics,Saarbruecken,Germany
Mirco Tribastone Stephen Gilmore (Eds.)
Computer
Performance Engineering
9th European Workshop, EPEW 2012
Munich, Germany, July 30, 2012, and
28th UK Workshop, UKPEW 2012
Edinburgh, UK, July 2, 2012
Revised Selected Papers
1 3
VolumeEditors
MircoTribastone
Ludwig-MaximiliansUniversity
DepartmentforInformatics
Oettingenstrasse67,80538Munich,Germany
E-mail:[email protected]fi.lmu.de
StephenGilmore
TheUniversityofEdinburgh
SchoolofInformatics
InformaticsForum
10CrichtonStreet,EdinburghEH89AB,UK
E-mail:[email protected]
ISSN0302-9743 e-ISSN1611-3349
ISBN978-3-642-36780-9 e-ISBN978-3-642-36781-6
DOI10.1007/978-3-642-36781-6
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2013931752
CRSubjectClassification(1998):C.4,D.2.7-9,C.2.1-2,C.2.4,I.6.3-6,J.2
LNCSSublibrary:SL2–ProgrammingandSoftwareEngineering
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Preface
This volume of LNCS contains papers from the 9th European Performance
Engineering Workshop held at the Ludwig Maximilians University of Munich,
Germany, on July 30, 2012, and papers from the 28th UK Performance Engi-
neering Workshop held at the University of Edinburgh on July 2, 2012.
The accepted papers reflect the breadth of interest across Europe in perfor-
mance engineering of software and hardware in computer systems. A healthy
rangeoftypesofpapersareinevidence:papersfromclassicalperformancemod-
ellingareassuchaswirelessnetworkprotocolsandparallelexecutionofscientific
codestohottopicssuchasenergy-awarecomputingtounexpectedventuresinto
rankingprofessionaltennisplayers.Inadditiontonewcasestudies,theaccepted
papers also present new techniques for dealing with the modelling challenges
brought about by the increasing complexity and scale of systems today.
WeextendourthankstoallmembersoftheProgramCommitteesforthetwo
workshops and all of the additional reviewers for their helpful and constructive
comments and reviews. The workshopswere enriched by keynote talks given by
IsiMitrani(EPEW)andKatinkaWolterandNigelThomas(UKPEW).Wewere
delighted to have their contributions.
WewouldliketoexpressourgratitudetoeveryoneinvolvedinmakingEPEW
2012 and UKPEW 2012 a success, in particular the staff of both the Ludwig
MaximiliansUniversityandtheUniversityofEdinburgh,whohelpedwithmany
organizational and logistical challenges. We are grateful to the EasyChair site
fortheirexcellentconferencesystemandtoSpringerfortheircontinuedsupport
of this workshop series. The UK Performance Engineering Workshop received
financial support from the Scottish Informatics and Computer Science Alliance
(SICSA) for which we are very grateful. Finally, we thank the authors of allthe
papers in this volume for their contribution and for meeting all of the deadlines
for the workshops.
July 2012 Mirco Tribastone
Stephen Gilmore
Organization
Chairs
EPEW Chair
Mirco Tribastone Ludwig Maximilians University of Munich,
Germany
UKPEW Chair
Stephen Gilmore University of Edinburgh, UK
Proceedings Chair
Robert Holton University of Bradford, UK
Publicity Chair
Steven Wright University of Warwick, UK
Program Committee
Steffen Becker University of Paderborn,Germany
Marco Bernardo University of Urbino, Italy
Luca Bortolussi University of Trieste, Italy
Jeremy Bradley Imperial College London, UK
Allan Clark University of Edinburgh, UK
Antinisca Di Marco University of L’Aquila, Italy
Nick Dingle University of Manchester, UK
Paulo Fernandes PUCRS, Brazil
Jean-Michel Forneau University of Versailles-Saint-Quentin, France
Vashti Galpin University of Edinburgh, UK
Nicholas Gast EPFL, Switzerland
Stephen Gilmore University of Edinburgh, UK
Richard Hayden Imperial College London, UK
Andras Horvath University of Turin, Italy
Samuel Kounev Karlsruhe Institute of Technology, Germany
Catalina M. Llado´ University of the Balearic Islands, Spain
Dorina Petriu Carleton University, Canada
Marco Scarpa University of Messina, Italy
Markus Siegle University of the Federal Armed Forces,
Munich, Germany
Miklos Telek Budapest University of Technology and
Economics, Hungary
VIII Organization
Nigel Thomas Newcastle University, UK
Mirco Tribastone Ludwig Maximilians University Munich,
Germany
Petr Tuma Charles University Prague, Czech Republic
Antonio Vallecillo University of Malaga, Spain
Katinka Wolter Free University of Berlin, Germany
Sponsoring Institutions
The Scottish Informatics and Computer Science Alliance (SICSA)
Table of Contents
EPEW Keynote
Trading Power Consumption against Performance by Reserving Blocks
of Servers ....................................................... 1
Isi Mitrani
EPEW Regular Papers
Operational Semantics for Product-Form Solution.................... 16
Maria Grazia Vigliotti
Moment Closures for Performance Models with Highly Non-linear
Rates........................................................... 32
Marcel C. Guenther, Anton Stefanek, and Jeremy T. Bradley
Scale-Freeness of SPA Models with Weighted Immediate Actions....... 48
Johann Schuster and Markus Siegle
Experimental Evaluation of the Performance-Influencing Factors of
Virtualized Storage Systems....................................... 63
Qais Noorshams, Samuel Kounev, and Ralf Reussner
Tradeoff between Accuracy and Efficiency in the Time-Parallel
Simulation of Monotone Systems................................... 80
J.M. Fourneau and F. Quessette
Compositional Approximate Markov Chain Aggregation for PEPA
Models ......................................................... 96
Dimitrios Milios and Stephen Gilmore
A Path Connection Availability Model for MANETs with Random
Waypoint Mobility ............................................... 111
Osama Younes and Nigel Thomas
UKPEW Keynote
Formal Performance Modelling: From Protocols to People............. 127
Nigel Thomas, Michael Harrison, Yishi Zhao, and Xiao Chen
UKPEW Regular Papers
SRN Models for Analysis of Multihop Wireless Ad Hoc Networks ...... 140
Osama Younes and Nigel Thomas
X Table of Contents
Don’t Just Go with the Flow: Cautionary Tales of Fluid Flow
Approximation .................................................. 156
Alireza Pourranjbar, Jane Hillston, and Luca Bortolussi
PCTMC Models of Wireless Sensor Network Protocols................ 172
Marcel C. Guenther and Jeremy T. Bradley
A Novel Approach to Energy Efficient Content Distribution with
BitTorrent ...................................................... 188
Matthew Forshaw and Nigel Thomas
Performance Modelling of Magnetohydrodynamics Codes ............. 197
R.F. Bird, S.A. Wright, D.A. Beckingsale, and S.A. Jarvis
Optimisation of Patch Distribution Strategies for AMR Applications ... 210
D.A. Beckingsale, O.F.J. Perks, W.P. Gaudin, J.A. Herdman, and
S.A. Jarvis
Energy Consumption in the Office.................................. 224
Anton Stefanek, Uli Harder, and Jeremy T. Bradley
On the (Page) Ranking of ProfessionalTennis Players ................ 237
Nicholas Dingle, William Knottenbelt, and Demetris Spanias
UKPEW Poster
Towards a Volunteer Cloud Architecture ............................ 248
Abdulelah Alwabel, Robert Walters, and Gary Wills
Author Index.................................................. 253
Trading Power Consumption
against Performance by Reserving Blocks
of Servers
Isi Mitrani
School of Computing Science, Newcastle University,NE1 7RU,UK
[email protected]
Abstract. Weconsidertheproblemofmanagingaservicecenterwhere
itisdesirabletokeeppowerconsumptionlow.Customersmaybepatient
or they may defect (i.e. leave the system) if waiting times are too long.
Several blocks of servers are designated as ‘reserves’. They are powered
up and down when the queue size increases above or falls below cer-
tain thresholds. Objective functions to be minimized take into account
the number of servers that are powered up, the number of occasions
whenblocksarepoweredupandaperformancemeasureinvolvingeither
waiting time or numberof jobs lost through defections. The question of
howtochoose theblocksof reservesisanswered byanalyzing asuitable
queueing model. Heuristic policies are proposed and numerical results
are presented.
Keywords: Cloud Computing, Power saving, Performance, Dynamic
server allocation, Reneging, Queueing models.
1 Introduction
Service centers are supporting the operations of major organizations.They also
form the basis of a business model, sometimes referred to as Cloud Comput-
ing, whereby a company makes money by selling computing services to paying
customers. A typical service center may contain thousands of computers, which
requirelargeamounts ofpowerto runandto keepcool(not tomentionthe hid-
den costs associated with greenhouse gas emissions). Consequently, it is impor-
tant to devise strategies for reducing the power consumption while maintaining
acceptable levels of performance.
Despite much research on the design of servers whose power consumption
is proportional to their utilization, progress in that direction has been limited.
Even with the best current designs, the power consumed by an idle server is
about 65% of its peak consumption [6]. Hence, the only realistic way to reduce
significantly the power consumption of a service center is to power down blocks
of serverswhenever that can be justified by the demand conditions. That is the
policy that this paper proposes to investigate.
Note the emphasis on ’blocks’: the servers in a large-scale service center
are usually grouped into logical or physical units of equal or unequal sizes.
M.TribastoneandS.Gilmore(Eds.):EPEW/UKPEW2012,LNCS7587,pp.1–15,2013.
(cid:2)c Springer-VerlagBerlinHeidelberg2013
2 I. Mitrani
The problem of managing these blocks efficiently is to ensure that they are
switched onreadily enoughto respondto increaseddemand, yet not leavethem
powered up unnecessarily when they are under-utilized.
The behaviour of users depends on the way the service center is run. In some
cases, submitted jobs are obliged to remain in the system until their service is
completed (e.g., users pay for services in advance). Then the objective to be
optimized would include some measure of performance such as the average re-
sponse time. If, on the other hand, long waits can cause jobs to depart before
receiving service, then a major part of the objective would be to minimize the
number of these ‘defections’. In both cases, a trade-off arises: the user experi-
ence is improved by increasing the number of powered-up servers, while power
consumption is lowered by increasing the number of powered-down servers.
Weproposeadynamicoperatingpolicywhereasubsetoftheavailableservers
are left powered on all the time, while the remainder is divided into k ‘reserve’
blocks. The latter are powered up and down depending on the number of jobs
in the system. The problem is to decide how many and how big should the
reserve blocks be. Answers are provided by analyzing two queueing models of
the system: with and without defections. Closed-form solutions are obtained in
both cases. These also lead to easily implementable and very efficient heuristic
policies.
There is a large volume of literature on improving the power consumption of
computer systems. For an overview and many references, see Berl et al [3]. A
system for monitoring demand and adaptive allocation of servers was proposed
byChase etal[4]. However,the setofstudies wherea specific dynamicpolicy is
quantitativelymodeledwithaviewtopredictingits behaviour,is muchsmaller.
It is to that set that the present paper belongs.
PerhapstheclosestmodelstoourswereexaminedbyArtalejoetal.[2],Gandhi
etal.[5],Mitrani[8,9],Schwartzetal[10]andMazzuccoetal[7].In[2]andin[5],
serversarepoweredupanddownoneatatime.Exactresultsareobtainedwhen
atmostoneservercanbeintheset-upmode(i.e.,intheprocessofbeingpowered
up), or when the number of servers is infinite. In [8], there is a single reserve
block with set-up times; no defections are allowed. The same model without
set-up times was subsequently examined in [10]. A similar system, again with
a single reserve block but allowing defections, was studied in [9]. Reneging was
also considered in [7], in the context of a static policy which employs a fixed
number of servers. The objective was to examine what that number should be.
For some further aspects of queues with defections, see for example Ancker and
Gafarian [1], and Ward and Glynn [11].
At the time when [8] and [9] appeared, the questionwas asked“Why a single
blockofreserves?”Couldonenotreducecostsfurtherbyallowingseveralblocks
of servers to be designated as reserves and be turned on and off dynamically
in response to different loading conditions? We now aim to answer that ques-
tion by evaluating to what extent the increased flexibility would lead to lower
costs. It should be pointed out that such a generalization comes at a price: in
order to maintain tractability, it is assumed that both the power-on and the
