Elastic Resource Management in Cloud Computing Platforms

175 Pages·2014·2.34 MB·English

by  Upendra Sharma

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Open Access Dissertations
5-2013
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Upendra Sharma
University of Massachusetts Amherst
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ELASTIC RESOURCE MANAGEMENT IN CLOUD COMPUTING
PLATFORMS
ADissertationPresented
by
UPENDRASHARMA
SubmittedtotheGraduateSchoolofthe
UniversityofMassachusettsAmherstinpartialfulfillment
oftherequirementsforthedegreeof
DOCTOROFPHILOSOPHY
May2013
ComputerScience
!c CopyrightbyUpendraSharma2013
AllRightsReserved
ELASTIC RESOURCE MANAGEMENT IN CLOUD COMPUTING
PLATFORMS
ADissertationPresented
by
UPENDRASHARMA
Approvedastostyleandcontentby:
PrashantShenoy,Chair
DonTowsley,Member
ArunVenkatramani,Member
MichaelZink,Member
SambitSahu,Member
LoriA.Clarke,DepartmentChair
ComputerScience
DedicatedtomyGurus...
ACKNOWLEDGMENTS
IwouldliketotakethisopportunitytoexpressmyheartfeltthankstomyrespectedPhD
advisor Prof. Prashant Shenoy without whose patience and guidance the doctorate would
have remained a dream. I have learnt a great deal from him, starting from conducting
systemsresearchtothingsliketimemanagement,patienceandtolerance.
Next, I am deeply indebted to Dr. Sambit Sahu who has been my mentor for the last
four years. I have closely worked with him since the beginning of my graduate career. His
guidanceandinvaluablecommentshaveledmetofinishmyPhDworkinatimelyfashion.
I am very grateful to Prof. Don Towsley from whom I learnt a great lot, specifically about
queueing theory and in general about the method of conducting research. I am grateful to
myothercommitteemembers,Prof. ArunVenkatramani,andProf. MichaelZink,fortheir
valuable inputs during the course of PhD. I also want to thank my collaborator Dr. Anees
Shaikh for all his help, guidance and motivation during the early part of my PhD and also
duringmyinternshipsatIBMWatson.
I would like to thank my colleagues Rahul Singh, Tim Woods, Emmanuel Cecchet
and Tian Guo with whom I have worked on several research projects. Rahul and I have
developed a system that performs dynamic capacity planning and provisioning taking into
account the non-stationarity in the workload mix. Emmanuel, Rahul and I have designed
and developed Dolly, a virtualization driven database replication system. Tian, Tim and I
developed Seagull, a system for cloud bursting applications from a private cloud to public
andback.
I am very grateful to my buddy Rahul Singh for all the help he gave me during the
courseofPhDaswellasforallthestimulatingandthoughtprovokingdiscussionseitherin
v
thecubicleorovertheteasessions. IamalsogratefultoothermembersoftheLASSgroup,
particularly to Navin Sharma, Himanshu Agarwal and Akshat Kumar with whom I have
spent countless hours discussing a range of topics from philosophy to technical research.
I also want to thank Jeremy Gummeson, Aditya Mishra and Gaul Niv with whom I have
had numerous philosophical and technical discussions over tea and coffee. I am indebted
toTylerTraffordforhisassistanceinmanagingthemachinesusedinmywork,toLeeanne
Leclerc for all the help in keeping my department and graduate school requirements on
track,andtoKarrenSaccoforhandlingalladministrativework;withtheirhelpallthenon-
research problems never looked like problems. My time in Amherst was made enjoyable
by the incredible group of friends I developed over the years. Specifically, I am grateful
to Siddharth Srivastav, Parthasarthi Valluri, Kshitij Neroorkar and Lokesh and also their
families for the fun filled sessions of racquet ball, squash, badminton and various other
socialgatherings.
I am deeply obliged to my parents, my sister Shruti and brother in law Saurabh for
patiently motivating me to pursue PhD and all the other assistance since the beginning; I
also thank my adorable younger brother Subodh for always being there when needed and
also for being a patient listener. I want to thank my old friends from India who motivated
me to pursue PhD, my fellows from IBM India Research Lab for all the good time I had
withthemduringtheirvisitstoUSandduringmyhometrips.
Last but not the least, I want to express my heartfelt thanks to my dear wife Ruchita,
who has helped, supported and motivated me throughout the course of PhD. This journey
wouldhavebeenimpossiblewithoutherpatienceandsacrifice.
vi
ABSTRACT
ELASTIC RESOURCE MANAGEMENT IN CLOUD COMPUTING
PLATFORMS
MAY2013
UPENDRASHARMA
B.S.,BOMBAYUNIVERSITY,MUMBAI,INDIA
M.S.,INDIANINSTITUTEOFTECHNOLOGYBOMBAY,INDIA
Ph.D.,UNIVERSITYOFMASSACHUSETTSAMHERST
Directedby: ProfessorPrashantShenoy
Large scale enterprise applications are known to observe dynamic workload; provi-
sioningcorrect capacity fortheseapplications remainsanimportantand challengingprob-
lem. Predicting high variability fluctuations in workload or the peak workload is diffi-
cult;erroneouspredictionsoftenleadtounder-utilizedsystemsorinsomesituationscause
temporarily outage of an otherwise well provisioned web-site. Consequently, rather than
provisioning server capacity to handle infrequent peak workloads, an alternate approach
of dynamically provisioning capacity on-the-fly in response to workload fluctuations has
becomepopular.
Cloudplatformsareparticularlysuitedforsuchapplicationsduetotheirabilitytoprovi-
sioncapacitywhenneededandchargeforusageonpay-per-usebasis. Cloudenvironments
enable elastic provisioning by providing a variety of hardware configurations as well as
mechanismstoaddorremoveservercapacity.
vii
The first part of this thesis presents Kingfisher, a cost-aware system that provides a
generalized provisioning framework for supporting elasticity in the cloud by (i) leverag-
ing multiple mechanisms to reduce the time to transition to new configurations, and (ii)
optimizingtheselectionofavirtualserverconfigurationthatminimizecost.
Majority of these enterprise applications, deployed as web applications, are distributed
or replicated with a multi-tier architecture. SLAs for such applications are often expressed
as a high percentile of a performance metric, for e.g. 99 percentile of end to end response
time is less than 1 sec. In the second part of this thesis I present a model driven tech-
nique which provisions a multi-tier application for such an SLA and is targeted for cloud
platforms.
EnterprisescriticallydependontheseapplicationsandoftenownlargeITinfrastructure
to support the regular operation of these applications. However, provisioning for a peak
load or for high percentile of response time could be prohibitively expensive. Thus there
is a need of hybrid cloud model, where the enterprise uses its own private resources for
the majority of its computing, but then “bursts” into the cloud when local resources are
insufficient. Idiscussanewsystem,namelySeagull,whichperformsdynamicprovisioning
overahybridcloudmodelbyenablingcloudbursting.
Finally, I describe a methodology to model the configuration patterns (i.e deployment
topologies) of different control plane services of a cloud management system itself. I
presentagenericmethodology,basedonempiricalprofiling,whichprovidesinitialdeploy-
ment configuration of a control plane service and also a mechanism which iteratively ad-
juststheconfigurationtoavoidviolationofcontrolplane’sServiceLevelObjective(SLO).
viii
TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ................................................... v
ABSTRACT ..............................................................vii
LISTOFTABLES......................................................... xv
LISTOFFIGURES ...................................................... xvi
CHAPTER
1. INTRODUCTION ...................................................... 1
1.1 BackgroundandMotivation........................................... 2
1.2 ThesisContributions................................................. 3
1.2.1 ContributionSummary ........................................ 3
1.2.2 CostAwareElasticityinCloud ................................. 4
1.2.3 ElasticProvisioningfortheTail................................. 4
1.2.4 CloudBursting............................................... 5
1.2.5 FlexibleAdaptiveControlPlaneforPrivateClouds ................ 6
1.3 ThesisOutline ...................................................... 6
2. RELATEDWORK ...................................................... 8
2.1 CloudComputing................................................... 8
2.2 DynamicResourceProvisioning ...................................... 10
2.3 Hybridcloud ...................................................... 11
3. COST-AWAREELASTICITYINTHECLOUD ........................... 13
3.1 Introduction ....................................................... 13
3.2 CloudBackgroundandProblemStatement............................. 16
3.2.1 InitialProvisioning .......................................... 18
ix