Energy Efficiency in Office Computing Environments
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Fakulät für Informatik und Mathematik Universität Passau, Germany Energy Efficiency in Office Computing Environments Andreas Berl Supervisor: Hermann de Meer A thesis submitted for Doctoral Degree March 2011 1. Reviewer: Prof. Hermann de Meer Professor of Computer Networks and Communications University of Passau Innstr. 43 94032 Passau, Germany Email: [email protected] Web: http://www.net.fim.uni-passau.de 2. Reviewer: Prof. David Hutchison Director of InfoLab21 and Professor of Computing Lancaster University LA1 4WA Lancaster, UK Email: [email protected] Web: http://www.infolab21.lancs.ac.uk Abstract The increasing cost of energy and the worldwide desire to reduce CO2 emissions has raised concern about the energy efficiency of information and communica- tion technology. Whilst research has focused on data centres recently, this thesis identifies office computing environments as significant consumers of energy. Office computing environments offer great potential for energy savings: On one hand, such environments consist of a large number of hosts. On the other hand, these hosts often remain turned on 24 hours per day while being underutilised or even idle. This thesis analyzes the energy consumption within office computing environments and suggests an energy-efficient virtualized office environment. The office environment is virtualized to achieve flexible virtualized office resources that enable an energy-based resource management. This resource management stops idle services and idle hosts from consuming resources within the office and consolidates utilised office services on office hosts. This increases the utilisation of some hosts while other hosts are turned off to save energy. The suggested architecture is based on a decentralized approach that can be applied to all kinds of office computing environments, even if no centralized data centre infrastructure is available. The thesis develops the architecture of the virtualized office environment together with an energy consumption model that is able to estimate the energy consumption of hosts and network within office environments. The model enables the energy- related comparison of ordinary and virtualized office environments, considering the energy-efficient management of services. Furthermore, this thesis evaluates energy efficiency and overhead of the suggested approach. First, it theoretically proves the energy efficiency of the virtualized of- fice environment with respect to the energy consumption model. Second, it uses iii Markov processes to evaluate the impact of user behaviour on the suggested ar- chitecture. Finally, the thesis develops a discrete-event simulation that enables the simulation and evaluation of office computing environments with respect to vary- ing virtualization approaches, resource management parameters, user behaviour, and office equipment. The evaluation shows that the virtualized office environ- ment saves more than half of the energy consumption within office computing environments, depending on user behaviour and office equipment. iv Acknowledgements I would like to express my sincere gratitude to my supervisor, Prof. Hermann de Meer, for the continuous support, guidance, and valuable feedback I received during the last few years. He introduced me to the world of research, entrusted me with many interesting and challenging tasks, and taught me how to successfully write scientific publications. Furthermore, I would like to thank Prof. David Hutchison for enabling my four months DAAD scholarship at Lancaster University in 2009. He heavily supported my research with valuable discussions and brought me in contact with researchers of related fields. Especially, I want to thank Dr. Andreas Mauthe for his excellent supervision during my time in Lancaster and Gareth Tyson for most interesting dis- cussions and giving me guided Lancaster tours. I also want to thank Dr. Nicholas Race and Dr. Johnathan Ishmael for their help and cooperation. My special thanks go to all of my colleagues at the University of Passau. Specif- ically, I want to thank Andreas Fischer and Patrick Wüchner for their support, insightful discussions, and reviews of my thesis. Finally, I want to thank my wife Saskia for her support during the last two years, endless discussions, and helpful reviews. v Contents List of Figures xi List of Tables xiii List of Definitions xv List of Acronyms xvii List of Symbols xix 1 Introduction 1 1.1 Energy consumption within office computing environments . .1 1.2 Solution approach . .2 1.3 Contribution of this thesis . .3 1.4 Thesis structure . .5 2 Related Work 7 2.1 Virtualization . .7 2.1.1 Host virtualization . .8 2.1.2 Network virtualization . 13 2.2 Energy efficiency in distributed environments . 16 2.2.1 Power-management features of hosts . 16 2.2.2 Grid and cluster computing . 17 2.2.3 Future home environments . 18 2.2.4 Internet Suspend/Resume . 19 2.2.5 Office power management . 20 2.2.6 Terminal servers and virtual desktop infrastructures . 21 vii CONTENTS 2.2.7 Cloud computing . 23 2.3 Power consumption models . 25 2.3.1 Host models . 25 2.3.2 Networked architecture models . 27 3 Energy-Efficient Office Design 31 3.1 Energy-saving potential . 31 3.2 Design principles . 34 3.3 Requirements and challenges . 37 4 Virtualized Office Environment Architecture 43 4.1 Virtualization of office resources . 43 4.1.1 PDE execution environments . 44 4.1.2 PDE migration . 45 4.1.3 Distributed resource management . 49 4.2 PDE management . 56 4.2.1 Energy states of hosts and PDEs . 56 4.2.2 Energy-optimal PDE management . 57 4.2.3 Service-optimal PDE management . 66 4.2.4 Energy and service-aware PDE management . 70 4.2.5 Energy efficiency and availability trade-off . 76 4.3 Resilience and security issues . 78 4.4 Architecture overview . 79 5 Energy Consumption Model 83 5.1 Host power consumption model . 84 5.1.1 Power consumption characteristic . 84 5.1.2 Ordinary host model . 87 5.1.3 Virtualized host model . 88 5.2 Network power consumption model . 90 5.2.1 Power consumption characteristic . 90 5.2.2 Ordinary network model . 91 5.2.3 Virtualized network model . 91 5.3 Office energy consumption . 93 viii CONTENTS 5.4 Energy efficiency proof . 94 6 Evaluation 99 6.1 User model . 100 6.2 Markov process . 102 6.2.1 Work time scenario and modelling . 103 6.2.2 PDE management . 103 6.2.3 Evaluation . 104 6.2.4 Conclusions . 110 6.3 Discrete-event simulation . 110 6.3.1 DES implementation and validation . 111 6.3.2 Parameter settings . 112 6.3.3 Evaluation . 117 6.3.4 Conclusions . 131 6.4 Measurements . 132 6.4.1 Power meter and systems under test . 132 6.4.2 Validation of the energy consumption model . 133 6.4.3 Energy states and PDE migration . 136 6.4.4 Conclusions . 137 7 Conclusions and Future Work 139 7.1 Main contributions and results . 139 7.2 Application and practical implications . 141 7.3 Possible extensions and integration with other solutions . 142 References 145 ix List of Figures 2.1 Aggregation and splitting of hardware resources . .8 2.2 Layers of system virtualization . 10 2.3 The eDonkey architecture . 15 2.4 FHE architecture . 19 3.1 Ordinary and energy-efficient office environment . 36 3.2 Data centres, office environments, and home environments [74] . 37 4.1 PDEs encapsulated within VMs . 44 4.2 Migration of a PDE . 47 4.3 A centralized P2P-overlay approach . 51 4.4 A pure P2P-overlay approach . 52 4.5 A hybrid P2P-overlay approach . 55 4.6 PDE utilisation states . 59 4.7 Lower bound of active hosts within different office environments . 65 4.8 PDE service within an ordinary office environment . 68 4.9 PDE service within a virtualized office environment . 69 4.10 Extended PDE utilisation states . 72 4.11 Architecture overview . 80 4.12 Ordinary and virtualized office environment . 81 5.1 Power consumption of servers and office hosts . 85 5.2 Power consumption of an ordinary host . 87 5.3 Power consumption of a virtualized host . 90 5.4 Power consumption of a virtualized network . 93 xi LIST OF FIGURES n Cidle 5.5 Relation of mean consolidation ratio and m := h ............... 96 Cidle 6.1 User model . 102 6.2 User and work time scenario modelled as generalised stochastic Petri net . 104 6.3 Probability of jobs . 106 6.4 Probability of remote users . 106 6.5 Probability that users turn their host off over night . 107 6.6 Interaction period . 108 6.7 Period without interaction . 108 6.8 Probability of jobs, with premote = 0 ....................... 109 6.9 Probability of remote users, with p job = 0.................... 109 6.10 Probability of remote users in the Markov process . 112 6.11 Probability of remote users in the DES . 112 6.12 Mean consolidation ratio, StrictTime scenario and ch = 2............ 118 6.13 Mean service ratio, StrictTime scenario and ch = 2 ............... 119 6.14 Mean service ratio between 7:50 h and 8:10 h, StrictTime scenario, ch = 2 . 119 6.15 Evaluation function, StrictTime scenario, ch = 2, a = 1, b = 2 . 120.