Energy efficiency in data centers and clouds:
Advances in Computerscarries on a tradition of excellence, presenting detailed coverage of innovations in computer hardware, software, theory, design, and applications.The book provides contributors with a medium in which they can explore their subjects in greater depth and breadth than journal arti...
Gespeichert in:
| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Academic Press
2016
|
| Ausgabe: | First edition |
| Schriftenreihe: | Advances in computers
Volume one hundred |
| Schlagworte: | |
| Online-Zugang: | TUM01 Inhaltsverzeichnis |
| Zusammenfassung: | Advances in Computerscarries on a tradition of excellence, presenting detailed coverage of innovations in computer hardware, software, theory, design, and applications.The book provides contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles typically allow. The articles included in this book will become standard references, with lasting value in this rapidly expanding field.Presents detailed coverage of recent innovations in computer hardware, software, theory, design, and applicationsIncludes in-depth surveys and tutorials on new computer technology pertaining to computing: combinatorial testing, constraint-based testing, and black-box testingWritten by well-known authors and researchers in the fieldIncludes extensive bibliographies with most chaptersPresents volumes devoted to single themes or subfields of computer science |
| Beschreibung: | Description based upon print version of record |
| Beschreibung: | 1 Online-Ressource (viii, 274 Seiten) |
| ISBN: | 9780128051733 |
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| 245 | 1 | 0 | |a Energy efficiency in data centers and clouds |c Edited by Ali R. Hurson, Hamid Sarbazi-Azad |
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| 264 | 1 | |a Amsterdam |b Academic Press |c 2016 | |
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| 490 | 0 | |a Advances in computers |v Volume one hundred | |
| 500 | |a Description based upon print version of record | ||
| 505 | 8 | |a Front Cover; Energy Efficiency in Data Centers and Clouds; Copyright; Contents; Preface; Chapter One: Power Management in Data Centers: Cost, Sustainability, and Demand Response; 1. Introduction; 1.1. Existing System Management Techniques in Data Centers; 1.2. Novel System Management Techniques in Data Centers; 2. Cost Minimization in Data Centers; 2.1. Related Work on DC Optimization and VM Resource Management; 2.2. Online Energy Budgeting; 2.2.1. Related Work on Power Budgeting and Energy Capping; 2.2.2. Problem Formulation; 2.2.3. Scheduling Algorithm (eBud) | |
| 505 | 8 | |a 2.2.4. Performance Evaluation of eBud2.3. Geographical Cost Minimization with Thermal Awareness (GreFar); 2.3.1. Related Work on Thermal-Aware Resource Management; 2.3.2. Problem Formulation; 2.3.3. Online Algorithm (GreFar); 2.3.4. Performance Evaluation of GreFar; 3. Sustainability: Water Efficiency; 3.1. Motivation; 3.2. Related Work; 3.3. Challenge; 3.3.1. Exploiting Temporal Diversity of Water Efficiency (WACE) [19]; 3.3.2. Optimizing Water Efficiency in Distributed DCs (GLB-WS) [20]; 3.4. Water Consumption in Data Centers | |
| 505 | 8 | |a 4. Sustainability: Exploiting Temporal Diversity of Water Efficiency (WACE)4.1. System Model of WACE; 4.2. Problem Formulation of WACE; 4.3. Online Algorithm (WACE); 4.4. Performance Evaluation of WACE; 5. Sustainability: Optimizing Water Efficiency in Distributed Data Centers (GLB-WS); 5.1. System Model of GLB-WS; 5.2. Problem Formulation of GLB-WS; 5.3. Scheduling Algorithm (GLB-WS); 5.4. Performance Evaluation of GLB-WS; 6. Demand Response of Geo-Distributed Data Centers: Real-Time Pricing Game Approach; 6.1. Motivation; 6.2. Challenge; 6.3. Related Work for Demand Response | |
| 505 | 8 | |a 6.4. DCs' Cost Minimization in Stage II6.5. Noncooperative Pricing Game in Stage I; 6.5.1. Utility Revenue's Model; 6.5.2. Utility Cost's Model; 6.5.3. Stage-I Pricing Game Formulation; 6.6. Two-Stage Stackelberg Game: Equilibria and Algorithm; 6.6.1. Backward Induction Method; 6.6.2. Distributed Algorithm; 6.7. Performance Evaluations; 7. Demand Response of Colocation Data Centers: A Reverse Auction Approach; 7.1. Motivation and Challenge; 7.2. Related Work for Colocation Demand Response; 7.3. System Model; 7.4. Algorithm: iCODE; 7.4.1. Deciding Tenants' Bids; 7.4.2. Deciding Winning Bids | |
| 505 | 8 | |a 7.5. Performance Analysis8. Conclusion and Open Challenges; Acknowledgments; References; About the Authors; Chapter Two: Energy-Efficient Big Data Analytics in Datacenters; 1. Introduction; 2. Datacenter and Cloud Computing; 2.1. Datacenter Architecture; 2.2. Cloud Computing; 2.2.1. Virtualization in Cloud Systems; 2.3. Datacenters for Cloud Computing Services; 2.3.1. Multicloud Systems; 3. Handling Big Data on Cloud-Based Datacenters; 3.1. Big Data; 3.2. Big Data and Cloud Computing; 3.3. Big Data Analytics on Datacenters; 3.3.1. Interconnection Networks; 3.3.2. Storage | |
| 505 | 8 | |a 3.3.3. Processing and Analysis Tools and Techniques | |
| 520 | 1 | |a Advances in Computerscarries on a tradition of excellence, presenting detailed coverage of innovations in computer hardware, software, theory, design, and applications.The book provides contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles typically allow. The articles included in this book will become standard references, with lasting value in this rapidly expanding field.Presents detailed coverage of recent innovations in computer hardware, software, theory, design, and applicationsIncludes in-depth surveys and tutorials on new computer technology pertaining to computing: combinatorial testing, constraint-based testing, and black-box testingWritten by well-known authors and researchers in the fieldIncludes extensive bibliographies with most chaptersPresents volumes devoted to single themes or subfields of computer science | |
| 653 | |a Electronic books | ||
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Datensatz im Suchindex
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| contents | Front Cover; Energy Efficiency in Data Centers and Clouds; Copyright; Contents; Preface; Chapter One: Power Management in Data Centers: Cost, Sustainability, and Demand Response; 1. Introduction; 1.1. Existing System Management Techniques in Data Centers; 1.2. Novel System Management Techniques in Data Centers; 2. Cost Minimization in Data Centers; 2.1. Related Work on DC Optimization and VM Resource Management; 2.2. Online Energy Budgeting; 2.2.1. Related Work on Power Budgeting and Energy Capping; 2.2.2. Problem Formulation; 2.2.3. Scheduling Algorithm (eBud) 2.2.4. Performance Evaluation of eBud2.3. Geographical Cost Minimization with Thermal Awareness (GreFar); 2.3.1. Related Work on Thermal-Aware Resource Management; 2.3.2. Problem Formulation; 2.3.3. Online Algorithm (GreFar); 2.3.4. Performance Evaluation of GreFar; 3. Sustainability: Water Efficiency; 3.1. Motivation; 3.2. Related Work; 3.3. Challenge; 3.3.1. Exploiting Temporal Diversity of Water Efficiency (WACE) [19]; 3.3.2. Optimizing Water Efficiency in Distributed DCs (GLB-WS) [20]; 3.4. Water Consumption in Data Centers 4. Sustainability: Exploiting Temporal Diversity of Water Efficiency (WACE)4.1. System Model of WACE; 4.2. Problem Formulation of WACE; 4.3. Online Algorithm (WACE); 4.4. Performance Evaluation of WACE; 5. Sustainability: Optimizing Water Efficiency in Distributed Data Centers (GLB-WS); 5.1. System Model of GLB-WS; 5.2. Problem Formulation of GLB-WS; 5.3. Scheduling Algorithm (GLB-WS); 5.4. Performance Evaluation of GLB-WS; 6. Demand Response of Geo-Distributed Data Centers: Real-Time Pricing Game Approach; 6.1. Motivation; 6.2. Challenge; 6.3. Related Work for Demand Response 6.4. DCs' Cost Minimization in Stage II6.5. Noncooperative Pricing Game in Stage I; 6.5.1. Utility Revenue's Model; 6.5.2. Utility Cost's Model; 6.5.3. Stage-I Pricing Game Formulation; 6.6. Two-Stage Stackelberg Game: Equilibria and Algorithm; 6.6.1. Backward Induction Method; 6.6.2. Distributed Algorithm; 6.7. Performance Evaluations; 7. Demand Response of Colocation Data Centers: A Reverse Auction Approach; 7.1. Motivation and Challenge; 7.2. Related Work for Colocation Demand Response; 7.3. System Model; 7.4. Algorithm: iCODE; 7.4.1. Deciding Tenants' Bids; 7.4.2. Deciding Winning Bids 7.5. Performance Analysis8. Conclusion and Open Challenges; Acknowledgments; References; About the Authors; Chapter Two: Energy-Efficient Big Data Analytics in Datacenters; 1. Introduction; 2. Datacenter and Cloud Computing; 2.1. Datacenter Architecture; 2.2. Cloud Computing; 2.2.1. Virtualization in Cloud Systems; 2.3. Datacenters for Cloud Computing Services; 2.3.1. Multicloud Systems; 3. Handling Big Data on Cloud-Based Datacenters; 3.1. Big Data; 3.2. Big Data and Cloud Computing; 3.3. Big Data Analytics on Datacenters; 3.3.1. Interconnection Networks; 3.3.2. Storage 3.3.3. Processing and Analysis Tools and Techniques |
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| indexdate | 2024-07-10T07:32:59Z |
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| isbn | 9780128051733 |
| language | English |
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| physical | 1 Online-Ressource (viii, 274 Seiten) |
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| series2 | Advances in computers |
| spelling | Hurson, Ali R. (DE-588)1028661932 edt Energy efficiency in data centers and clouds Edited by Ali R. Hurson, Hamid Sarbazi-Azad First edition Amsterdam Academic Press 2016 1 Online-Ressource (viii, 274 Seiten) txt rdacontent c rdamedia cr rdacarrier Advances in computers Volume one hundred Description based upon print version of record Front Cover; Energy Efficiency in Data Centers and Clouds; Copyright; Contents; Preface; Chapter One: Power Management in Data Centers: Cost, Sustainability, and Demand Response; 1. Introduction; 1.1. Existing System Management Techniques in Data Centers; 1.2. Novel System Management Techniques in Data Centers; 2. Cost Minimization in Data Centers; 2.1. Related Work on DC Optimization and VM Resource Management; 2.2. Online Energy Budgeting; 2.2.1. Related Work on Power Budgeting and Energy Capping; 2.2.2. Problem Formulation; 2.2.3. Scheduling Algorithm (eBud) 2.2.4. Performance Evaluation of eBud2.3. Geographical Cost Minimization with Thermal Awareness (GreFar); 2.3.1. Related Work on Thermal-Aware Resource Management; 2.3.2. Problem Formulation; 2.3.3. Online Algorithm (GreFar); 2.3.4. Performance Evaluation of GreFar; 3. Sustainability: Water Efficiency; 3.1. Motivation; 3.2. Related Work; 3.3. Challenge; 3.3.1. Exploiting Temporal Diversity of Water Efficiency (WACE) [19]; 3.3.2. Optimizing Water Efficiency in Distributed DCs (GLB-WS) [20]; 3.4. Water Consumption in Data Centers 4. Sustainability: Exploiting Temporal Diversity of Water Efficiency (WACE)4.1. System Model of WACE; 4.2. Problem Formulation of WACE; 4.3. Online Algorithm (WACE); 4.4. Performance Evaluation of WACE; 5. Sustainability: Optimizing Water Efficiency in Distributed Data Centers (GLB-WS); 5.1. System Model of GLB-WS; 5.2. Problem Formulation of GLB-WS; 5.3. Scheduling Algorithm (GLB-WS); 5.4. Performance Evaluation of GLB-WS; 6. Demand Response of Geo-Distributed Data Centers: Real-Time Pricing Game Approach; 6.1. Motivation; 6.2. Challenge; 6.3. Related Work for Demand Response 6.4. DCs' Cost Minimization in Stage II6.5. Noncooperative Pricing Game in Stage I; 6.5.1. Utility Revenue's Model; 6.5.2. Utility Cost's Model; 6.5.3. Stage-I Pricing Game Formulation; 6.6. Two-Stage Stackelberg Game: Equilibria and Algorithm; 6.6.1. Backward Induction Method; 6.6.2. Distributed Algorithm; 6.7. Performance Evaluations; 7. Demand Response of Colocation Data Centers: A Reverse Auction Approach; 7.1. Motivation and Challenge; 7.2. Related Work for Colocation Demand Response; 7.3. System Model; 7.4. Algorithm: iCODE; 7.4.1. Deciding Tenants' Bids; 7.4.2. Deciding Winning Bids 7.5. Performance Analysis8. Conclusion and Open Challenges; Acknowledgments; References; About the Authors; Chapter Two: Energy-Efficient Big Data Analytics in Datacenters; 1. Introduction; 2. Datacenter and Cloud Computing; 2.1. Datacenter Architecture; 2.2. Cloud Computing; 2.2.1. Virtualization in Cloud Systems; 2.3. Datacenters for Cloud Computing Services; 2.3.1. Multicloud Systems; 3. Handling Big Data on Cloud-Based Datacenters; 3.1. Big Data; 3.2. Big Data and Cloud Computing; 3.3. Big Data Analytics on Datacenters; 3.3.1. Interconnection Networks; 3.3.2. Storage 3.3.3. Processing and Analysis Tools and Techniques Advances in Computerscarries on a tradition of excellence, presenting detailed coverage of innovations in computer hardware, software, theory, design, and applications.The book provides contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles typically allow. The articles included in this book will become standard references, with lasting value in this rapidly expanding field.Presents detailed coverage of recent innovations in computer hardware, software, theory, design, and applicationsIncludes in-depth surveys and tutorials on new computer technology pertaining to computing: combinatorial testing, constraint-based testing, and black-box testingWritten by well-known authors and researchers in the fieldIncludes extensive bibliographies with most chaptersPresents volumes devoted to single themes or subfields of computer science Electronic books Sarbazi-Azad, Hamid edt Erscheint auch als Druck-Ausgabe, Hardcover 978-0-12-804778-1 DE-601 pdf/application http://www.gbv.de/dms/bowker/toc/9780128047781.pdf Inhaltsverzeichnis |
| spellingShingle | Energy efficiency in data centers and clouds Front Cover; Energy Efficiency in Data Centers and Clouds; Copyright; Contents; Preface; Chapter One: Power Management in Data Centers: Cost, Sustainability, and Demand Response; 1. Introduction; 1.1. Existing System Management Techniques in Data Centers; 1.2. Novel System Management Techniques in Data Centers; 2. Cost Minimization in Data Centers; 2.1. Related Work on DC Optimization and VM Resource Management; 2.2. Online Energy Budgeting; 2.2.1. Related Work on Power Budgeting and Energy Capping; 2.2.2. Problem Formulation; 2.2.3. Scheduling Algorithm (eBud) 2.2.4. Performance Evaluation of eBud2.3. Geographical Cost Minimization with Thermal Awareness (GreFar); 2.3.1. Related Work on Thermal-Aware Resource Management; 2.3.2. Problem Formulation; 2.3.3. Online Algorithm (GreFar); 2.3.4. Performance Evaluation of GreFar; 3. Sustainability: Water Efficiency; 3.1. Motivation; 3.2. Related Work; 3.3. Challenge; 3.3.1. Exploiting Temporal Diversity of Water Efficiency (WACE) [19]; 3.3.2. Optimizing Water Efficiency in Distributed DCs (GLB-WS) [20]; 3.4. Water Consumption in Data Centers 4. Sustainability: Exploiting Temporal Diversity of Water Efficiency (WACE)4.1. System Model of WACE; 4.2. Problem Formulation of WACE; 4.3. Online Algorithm (WACE); 4.4. Performance Evaluation of WACE; 5. Sustainability: Optimizing Water Efficiency in Distributed Data Centers (GLB-WS); 5.1. System Model of GLB-WS; 5.2. Problem Formulation of GLB-WS; 5.3. Scheduling Algorithm (GLB-WS); 5.4. Performance Evaluation of GLB-WS; 6. Demand Response of Geo-Distributed Data Centers: Real-Time Pricing Game Approach; 6.1. Motivation; 6.2. Challenge; 6.3. Related Work for Demand Response 6.4. DCs' Cost Minimization in Stage II6.5. Noncooperative Pricing Game in Stage I; 6.5.1. Utility Revenue's Model; 6.5.2. Utility Cost's Model; 6.5.3. Stage-I Pricing Game Formulation; 6.6. Two-Stage Stackelberg Game: Equilibria and Algorithm; 6.6.1. Backward Induction Method; 6.6.2. Distributed Algorithm; 6.7. Performance Evaluations; 7. Demand Response of Colocation Data Centers: A Reverse Auction Approach; 7.1. Motivation and Challenge; 7.2. Related Work for Colocation Demand Response; 7.3. System Model; 7.4. Algorithm: iCODE; 7.4.1. Deciding Tenants' Bids; 7.4.2. Deciding Winning Bids 7.5. Performance Analysis8. Conclusion and Open Challenges; Acknowledgments; References; About the Authors; Chapter Two: Energy-Efficient Big Data Analytics in Datacenters; 1. Introduction; 2. Datacenter and Cloud Computing; 2.1. Datacenter Architecture; 2.2. Cloud Computing; 2.2.1. Virtualization in Cloud Systems; 2.3. Datacenters for Cloud Computing Services; 2.3.1. Multicloud Systems; 3. Handling Big Data on Cloud-Based Datacenters; 3.1. Big Data; 3.2. Big Data and Cloud Computing; 3.3. Big Data Analytics on Datacenters; 3.3.1. Interconnection Networks; 3.3.2. Storage 3.3.3. Processing and Analysis Tools and Techniques |
| title | Energy efficiency in data centers and clouds |
| title_auth | Energy efficiency in data centers and clouds |
| title_exact_search | Energy efficiency in data centers and clouds |
| title_full | Energy efficiency in data centers and clouds Edited by Ali R. Hurson, Hamid Sarbazi-Azad |
| title_fullStr | Energy efficiency in data centers and clouds Edited by Ali R. Hurson, Hamid Sarbazi-Azad |
| title_full_unstemmed | Energy efficiency in data centers and clouds Edited by Ali R. Hurson, Hamid Sarbazi-Azad |
| title_short | Energy efficiency in data centers and clouds |
| title_sort | energy efficiency in data centers and clouds |
| url | http://www.gbv.de/dms/bowker/toc/9780128047781.pdf |
| work_keys_str_mv | AT hursonalir energyefficiencyindatacentersandclouds AT sarbaziazadhamid energyefficiencyindatacentersandclouds |