Optimization of Resource Allocation on the Cloud - The DIstributed

103 Pages·2013·2.5 MB·English

by  Kamil Figiela

AGH University of Science and Technology
in Kraków
Faculty of Computer Science, Electronics and Telecommunications
Department of Computer Science
Master of Science Thesis
Optimization of Resource Allocation
on the Cloud
Author: Supervisor:
Kamil Figiela dr inż. Maciej Malawski
Kraków, August 2013
Aware of criminal liability for making untrue statements I declare that the following
thesis was written personally by myself and that I did not use any sources but the ones
mentioned in the dissertation itself.
ii
Akademia Górniczo-Hutnicza
im. Stanisława Staszica w Krakowie
Wydział Informatyki, Elektroniki i Telekomunikacji
Katedra Informatyki
Praca Magisterska
Optymalizacja alokacji zasobów
w chmurze obliczeniowej
Autor: Promotor:
Kamil Figiela dr inż. Maciej Malawski
Kraków, Sierpień 2013
Oświadczam,świadomyodpowiedzialnościkarnejzapoświadczenienieprawdy,żeniniejszą
pracę dyplomową wykonałem osobiście i samodzielnie i że nie korzystałem ze źródeł in-
nych niż wymienione w pracy.
ii
Abstract
Nowdays, hardly any science can be done without a computing infrastructure, and cloud
systems are regarded by the scientific community as a potential source of low-cost com-
puting resources that can be provisioned on-demand according to pay-per-use model.
Running scientific applications on the cloud imposes the monetary cost of the computa-
tion that should be subject to optimization as the funding is usually limited.
We address the problem of resource allocation on multiple cloud platforms formulated
as a mixed integer non-linear programming problem (MINLP). We optimize scheduling
of bag of tasks applications and workflows under the deadline constraint. The optimiza-
tion models implemented in AMPL modeling language allow us to apply leading solvers
such as Cbc and CPLEX. We assume multiple IaaS clouds with heterogenous VM in-
stances, with limited number of instances per cloud and hourly billing. Our objective,
thetotalcost, includescomputationcostaswellasdatatransferchargeswhichmayhave
significant contribution to the total cost.
The results illustrate typical problems when making decisions on deployment planning
on clouds and how they can be addressed using optimization techniques. We indicate
howoptimizationofresourceallocationmaybeusedbyend-userstominimizetheircosts
or by resellers for a profit.

Acknowledgements
Foremost, I would like to express my sincere gratitude to my advisor Maciej Malawski
for the continuous support of my M.Sc. study and research, for his patience, motivation,
enthusiasm, and immense knowledge. His guidance helped me in all the time of research
and writing of this thesis.
Besides my advisor, I would also like to thank my parents for their endless love and
support. Last, but not least, I would also like to thank Jarek Nabrzyski from University
of Notre Dame for giving me opportunity to experience real research work two years ago.
This thesis was realized partially in the framework of the project Virtual Physiologi-
cal Human: Sharing for Healthcare (VPH-Share) - partially funded by the European
Commission under the Information Communication Technologies Programme (contract
number 269978).

Contents
Abstract iii
Acknowledgements v
List of Figures ix
List of Tables xi
1 Introduction 1
1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Scientific applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Bag of tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Introduction to Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Service models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.2 Deployment models. . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3 IaaS compute cloud . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Goals of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 State of the art review 9
2.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Mathematical programming using AMPL 13
3.1 Mathematical Programming . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Problem classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 AMPL: A Mathematical Programming Language . . . . . . . . . . . . . . 15
3.3.1 Available solvers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Example – Whiskas Cat Food Problem . . . . . . . . . . . . . . . . . . . . 16
3.4.1 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4.2 Problem formulation using AMPL . . . . . . . . . . . . . . . . . . 19
3.5 Example – Shift Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5.1 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5.2 Problem formulation using AMPL . . . . . . . . . . . . . . . . . . 24
vii
Contents viii
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 Bag of tasks optimization 27
4.1 Application model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 Infrastructure model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.3.1 Input data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3.2 Auxiliary parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.3 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.4 Formulation of constraints and objectives . . . . . . . . . . . . . . 32
4.3.5 Solver choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.4 Experiments and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.4.1 Private + infinite public clouds . . . . . . . . . . . . . . . . . . . . 35
4.4.2 Private + finite public clouds . . . . . . . . . . . . . . . . . . . . . 36
4.4.3 Overlapping computation and data transfers . . . . . . . . . . . . . 37
4.4.4 Identifying special cases . . . . . . . . . . . . . . . . . . . . . . . . 38
4.5 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.6 Impact of dynamic environment . . . . . . . . . . . . . . . . . . . . . . . . 41
4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5 Workflow optimization 45
5.1 Application model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2 Problem formulation using AMPL . . . . . . . . . . . . . . . . . . . . . . 46
5.2.1 Input data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.2.2 Auxiliary parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.2.3 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.2.4 Formulation of objectives . . . . . . . . . . . . . . . . . . . . . . . 48
5.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6 Conclusions and future work 59
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
A Source Code 61
B Publications 63
Bibliography 85