Energy Efficient Distributed Computing Systems

Name: Energy Efficient Distributed Computing Systems
Brand: Wiley
Price: 126.99 EUR
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Albert Y. Zomaya Young Choon Lee(Autor*in)

Wiley (Verlag)

Erschienen am 6. August 2012

856 Seiten

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Inhalt

Intro
ENERGY-EFFICIENT DISTRIBUTED COMPUTING SYSTEMS
CONTENTS
PREFACE
ACKNOWLEDGMENTS
CONTRIBUTORS
1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSOR COMPUTERS WITH ENERGY AND TIME CONSTRAINTS
1.1 Introduction
1.1.1 Energy Consumption
1.1.2 Power Reduction
1.1.3 Dynamic Power Management
1.1.4 Task Scheduling with Energy and Time Constraints
1.1.5 Chapter Outline
1.2 Preliminaries
1.2.1 Power Consumption Model
1.2.2 Problem Definitions
1.2.3 Task Models
1.2.4 Processor Models
1.2.5 Scheduling Models
1.2.6 Problem Decomposition
1.2.7 Types of Algorithms
1.3 Problem Analysis
1.3.1 Schedule Length Minimization
1.3.1.1 Uniprocessor computers
1.3.1.2 Multiprocessor computers
1.3.2 Energy Consumption Minimization
1.3.2.1 Uniprocessor computers
1.3.2.2 Multiprocessor computers
1.3.3 Strong NP-Hardness
1.3.4 Lower Bounds
1.3.5 Energy-Delay Trade-off
1.4 Pre-Power-Determination Algorithms
1.4.1 Overview
1.4.2 Performance Measures
1.4.3 Equal-Time Algorithms and Analysis
1.4.3.1 Schedule length minimization
1.4.3.2 Energy consumption minimization
1.4.4 Equal-Energy Algorithms and Analysis
1.4.4.1 Schedule length minimization
1.4.4.2 Energy consumption minimization
1.4.5 Equal-Speed Algorithms and Analysis
1.4.5.1 Schedule length minimization
1.4.5.2 Energy consumption minimization
1.4.6 Numerical Data
1.4.7 Simulation Results
1.5 Post-Power-Determination Algorithms
1.5.1 Overview
1.5.2 Analysis of List Scheduling Algorithms
1.5.2.1 Analysis of algorithm LS
1.5.2.2 Analysis of algorithm LRF
1.5.3 Application to Schedule Length Minimization
1.5.4 Application to Energy Consumption Minimization
1.5.5 Numerical Data
1.5.6 Simulation Results
1.6 Summary and Further Research
References
2 POWER-AWARE HIGH PERFORMANCE COMPUTING
2.1 Introduction
2.2 Background
2.2.1 Current Hardware Technology and Power Consumption
2.2.1.1 Processor power
2.2.1.2 Memory subsystem power
2.2.2 Performance
2.2.3 Energy Efficiency
2.3 Related Work
2.3.1 Power Profiling
2.3.1.1 Simulator-based power estimation
2.3.1.2 Direct measurements
2.3.1.3 Event-based estimation
2.3.2 Performance Scalability on Power-Aware Systems
2.3.3 Adaptive Power Allocation for Energy-Efficient Computing
2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications
2.4.1 Design and Implementation of PowerPack
2.4.1.1 Overview
2.4.1.2 Fine-grain systematic power measurement
2.4.1.3 Automatic power profiling and code synchronization
2.4.2 Power Profiles of HPC Applications and Systems
2.4.2.1 Power distribution over components
2.4.2.2 Power dynamics of applications
2.4.2.3 Power bounds on HPC systems
2.4.2.4 Power versus dynamic voltage and frequency scaling
2.5 Power-Aware Speedup Model
2.5.1 Power-Aware Speedup
2.5.1.1 Sequential execution time for a single workload T1(w, f )
2.5.1.2 Sequential execution time for an ON-chip/OFF-chip workload
2.5.1.3 Parallel execution time on N processors for an ON-/OFF-chip workload with DOP = i
2.5.1.4 Power-aware speedup for DOP and ON-/OFF-chip workloads
2.5.2 Model Parametrization and Validation
2.5.2.1 Coarse-grain parametrization and validation
2.5.2.2 Fine-grain parametrization and validation
2.6 Model Usages
2.6.1 Identification of Optimal System Configurations
2.6.2 PAS-Directed Energy-Driven Runtime Frequency Scaling
2.7 Conclusion
References
3 ENERGY EFFICIENCY IN HPC SYSTEMS
3.1 Introduction
3.2 Background and Related Work
3.2.1 CPU Power Management
3.2.1.1 OS-level CPU power management
3.2.1.2 Workload-level CPU power management
3.2.1.3 Cluster-level CPU power management
3.2.2 Component-Based Power Management
3.2.2.1 Memory subsystem
3.2.2.2 Storage subsystem
3.2.3 Thermal-Conscious Power Management
3.2.4 Power Management in Virtualized Datacenters
3.3 Proactive, Component-Based Power Management
3.3.1 Job Allocation Policies
3.3.2 Workload Profiling
3.4 Quantifying Energy Saving Possibilities
3.4.1 Methodology
3.4.2 Component-Level Power Requirements
3.4.3 Energy Savings
3.5 Evaluation of the Proposed Strategies
3.5.1 Methodology
3.5.2 Workloads
3.5.3 Metrics
3.6 Results
3.7 Concluding Remarks
3.8 Summary
References
4 A STOCHASTIC FRAMEWORK FOR HIERARCHICAL SYSTEM-LEVEL POWER MANAGEMENT
4.1 Introduction
4.2 Related Work
4.3 A Hierarchical DPM Architecture
4.4 Modeling
4.4.1 Model of the Application Pool
4.4.2 Model of the Service Flow Control
4.4.3 Model of the Simulated Service Provider
4.4.4 Modeling Dependencies between SPs
4.5 Policy Optimization
4.5.1 Mathematical Formulation
4.5.2 Optimal Time-Out Policy for Local Power Manager
4.6 Experimental Results
4.7 Conclusion
References
5 ENERGY-EFFICIENT RESERVATION INFRASTRUCTURE FOR GRIDS, CLOUDS, AND NETWORKS
5.1 Introduction
5.2 Related Works
5.2.1 Server and Data Center Power Management
5.2.2 Node Optimizations
5.2.3 Virtualization to Improve Energy Efficiency
5.2.4 Energy Awareness in Wired Networking Equipment
5.2.5 Synthesis
5.3 ERIDIS: Energy-Efficient Reservation Infrastructure for Large-Scale Distributed Systems
5.3.1 ERIDIS Architecture
5.3.2 Management of the Resource Reservations
5.3.3 Resource Management and On/Off Algorithms
5.3.4 Energy-Consumption Estimates
5.3.5 Prediction Algorithms
5.4 EARI: Energy-Aware Reservation Infrastructure for Data Centers and Grids
5.4.1 EARI's Architecture
5.4.2 Validation of EARI on Experimental Grid Traces
5.5 GOC: Green Open Cloud
5.5.1 GOC's Resource Manager Architecture
5.5.2 Validation of the GOC Framework
5.6 HERMES: High Level Energy-Aware Model for Bandwidth Reservation in End-To-End Networks
5.6.1 HERMES' Architecture
5.6.2 The Reservation Process of HERMES
5.6.3 Discussion
5.7 Summary
References
6 ENERGY-EFFICIENT JOB PLACEMENT ON CLUSTERS, GRIDS, AND CLOUDS
6.1 Problem and Motivation
6.1.1 Context
6.1.2 Chapter Roadmap
6.2 Energy-Aware Infrastructures
6.2.1 Buildings
6.2.2 Context-Aware Buildings
6.2.3 Cooling
6.3 Current Resource Management Practices
6.3.1 Widely Used Resource Management Systems
6.3.2 Job Requirement Description
6.4 Scientific and Technical Challenges
6.4.1 Theoretical Difficulties
6.4.2 Technical Difficulties
6.4.3 Controlling and Tuning Jobs
6.5 Energy-Aware Job Placement Algorithms
6.5.1 State of the Art
6.5.2 Detailing One Approach
6.6 Discussion
6.6.1 Open Issues and Opportunities
6.6.2 Obstacles for Adoption in Production
6.7 Conclusion
References
7 COMPARISON AND ANALYSIS OF GREEDY ENERGY-EFFICIENT SCHEDULING ALGORITHMS FOR COMPUTATIONAL GRIDS
7.1 Introduction
7.2 Problem Formulation
7.2.1 The System Model
7.2.1.1 PEs
7.2.1.2 DVS
7.2.1.3 Tasks
7.2.1.4 Preliminaries
7.2.2 Formulating the Energy-Makespan Minimization Problem
7.3 Proposed Algorithms
7.3.1 Greedy Heuristics
7.3.1.1 Greedy heuristic scheduling algorithm
7.3.1.2 Greedy-min
7.3.1.3 Greedy-deadline
7.3.1.4 Greedy-max
7.3.1.5 MaxMin
7.3.1.6 ObFun
7.3.1.7 MinMin StdDev
7.3.1.8 MinMax StdDev
7.4 Simulations, Results, and Discussion
7.4.1 Workload
7.4.2 Comparative Results
7.4.2.1 Small-size problems
7.4.2.2 Large-size problems
7.5 Related Works
7.6 Conclusion
References
8 TOWARD ENERGY-AWARE SCHEDULING USING MACHINE LEARNING
8.1 Introduction
8.1.1 Energetic Impact of the Cloud
8.1.2 An Intelligent Way to Manage Data Centers
8.1.3 Current Autonomic Computing Techniques
8.1.4 Power-Aware Autonomic Computing
8.1.5 State of the Art and Case Study
8.2 Intelligent Self-Management
8.2.1 Classical AI Approaches
8.2.1.1 Heuristic algorithms
8.2.1.2 AI planning
8.2.1.3 Semantic techniques
8.2.1.4 Expert systems and genetic algorithms
8.2.2 Machine Learning Approaches
8.2.2.1 Instance-based learning
8.2.2.2 Reinforcement learning
8.2.2.3 Feature and example selection
8.3 Introducing Power-Aware Approaches
8.3.1 Use of Virtualization
8.3.2 Turning On and Off Machines
8.3.3 Dynamic Voltage and Frequency Scaling
8.3.4 Hybrid Nodes and Data Centers
8.4 Experiences of Applying ML on Power-Aware Self-Management
8.4.1 Case Study Approach
8.4.2 Scheduling and Power Trade-Off
8.4.3 Experimenting with Power-Aware Techniques
8.4.4 Applying Machine Learning
8.4.5 Conclusions from the Experiments
8.5 Conclusions on Intelligent Power-Aware Self-Management
References
9 ENERGY EFFICIENCY METRICS FOR DATA CENTERS
9.1 Introduction
9.1.1 Background
9.1.2 Data Center Energy Use
9.1.3 Data Center Characteristics
9.1.3.1 Electric power
9.1.3.2 Heat removal
9.1.4 Energy Efficiency
9.2 Fundamentals of Metrics
9.2.1 Demand and Constraints on Data Center Operators
9.2.2 Metrics
9.2.2.1 Criteria for good metrics
9.2.2.2 Methodology
9.2.2.3 Stability of metrics
9.3 Data Center Energy Efficiency
9.3.1 Holistic IT Efficiency Metrics
9.3.1.1 Fixed versus proportional overheads
9.3.1.2 Power versus energy
9.3.1.3 Performance versus productivity
9.3.2 Code of Conduct
9.3.2.1 Environmental statement
9.3.2.2 Problem statement
9.3.2.3 Scope of the CoC
9.3.2.4 Aims and objectives of CoC
9.3.3 Power Use in Data Centers
9.3.3.1 Data center IT power to utility power relationship
9.3.3.2 Chiller efficiency and external temperature
9.4 Available Metrics
9.4.1 The Green Grid
9.4.1.1 Power usage effectiveness (PUE)
9.4.1.2 Data center efficiency (DCE)
9.4.1.3 Data center infrastructure efficiency (DCiE)
9.4.1.4 Data center productivity (DCP)
9.4.2 McKinsey
9.4.3 Uptime Institute
9.4.3.1 Site infrastructure power overhead multiplier (SI-POM)
9.4.3.2 IT hardware power overhead multiplier (H-POM)
9.4.3.3 DC hardware compute load per unit of computing work done
9.4.3.4 Deployed hardware utilization ratio (DH-UR)
9.4.3.5 Deployed hardware utilization efficiency (DH-UE)
9.5 Harmonizing Global Metrics for Data Center Energy Efficiency
References
10 AUTONOMIC GREEN COMPUTING IN LARGE-SCALE DATA CENTERS
10.1 Introduction
10.2 Related Technologies and Techniques
10.2.1 Power Optimization Techniques in Data Centers
10.2.2 Design Model
10.2.3 Networks
10.2.4 Data Center Power Distribution
10.2.5 Data Center Power-Efficient Metrics
10.2.6 Modeling Prototype and Testbed
10.2.7 Green Computing
10.2.8 Energy Proportional Computing
10.2.9 Hardware Virtualization Technology
10.2.10 Autonomic Computing
10.3 Autonomic Green Computing: A Case Study
10.3.1 Autonomic Management Platform
10.3.1.1 Platform architecture
10.3.1.2 DEVS-based modeling and simulation platform
10.3.1.3 Workload generator
10.3.2 Model Parameter Evaluation
10.3.2.1 State transitioning overhead
10.3.2.2 VM template evaluation
10.3.2.3 Scalability analysis
10.3.3 Autonomic Power Efficiency Management Algorithm (Performance Per Watt)
10.3.4 Simulation Results and Evaluation
10.3.4.1 Analysis of energy and performance trade-offs
10.4 Conclusion and Future Directions
References
11 ENERGY AND THERMAL AWARE SCHEDULING IN DATA CENTERS
11.1 Introduction
11.2 Related Work
11.3 Intermachine Scheduling
11.3.1 Performance and Power Profile of VMs
11.3.2 Architecture
11.3.2.1 vgnode
11.3.2.2 vgxen
11.3.2.3 vgdom
11.3.2.4 vgserv
11.4 Intramachine Scheduling
11.4.1 Air-Forced Thermal Modeling and Cost
11.4.2 Cooling Aware Dynamic Workload Scheduling
11.4.3 Scheduling Mechanism
11.4.4 Cooling Costs Predictor
11.5 Evaluation
11.5.1 Intermachine Scheduler (vGreen)
11.5.2 Heterogeneous Workloads
11.5.2.1 Comparison with DVFS policies
11.5.2.2 Homogeneous workloads
11.5.3 Intramachine Scheduler (Cool and Save)
11.5.3.1 Results
11.5.3.2 Overhead of CAS
11.6 Conclusion
References
12 QOS-AWARE POWER MANAGEMENT IN DATA CENTERS
12.1 Introduction
12.2 Problem Classification
12.2.1 Objective and Constraint
12.2.2 Scope and Time Granularities
12.2.3 Methodology
12.2.4 Power Management Mechanism
12.3 Energy Efficiency
12.3.1 Energy-Efficiency Metrics
12.3.2 Improving Energy Efficiency
12.3.2.1 Energy minimization with performance guarantee
12.3.2.2 Performance maximization under power budget
12.3.2.3 Trade-off between power and performance
12.3.3 Energy-Proportional Computing
12.4 Power Capping
12.5 Conclusion
References
13 ENERGY-EFFICIENT STORAGE SYSTEMS FOR DATA CENTERS
13.1 Introduction
13.2 Disk Drive Operation and Disk Power
13.2.1 An Overview of Disk Drives
13.2.2 Sources of Disk Power Consumption
13.2.3 Disk Activity and Power Consumption
13.3 Disk and Storage Power Reduction Techniques
13.3.1 Exploiting the STANDBY State
13.3.2 Reducing Seek Activity
13.3.3 Achieving Energy Proportionality
13.3.3.1 Hardware approaches
13.3.3.2 Software approaches
13.4 Using Nonvolatile Memory and Solid-State Disks
13.5 Conclusions
References
14 AUTONOMIC ENERGY/PERFORMANCE OPTIMIZATIONS FOR MEMORY IN SERVERS
14.1 Introduction
14.2 Classifications of Dynamic Power Management Techniques
14.2.1 Heuristic and Predictive Techniques
14.2.2 QoS and Energy Trade-Offs
14.3 Applications of Dynamic Power Management (DPM)
14.3.1 Power Management of System Components in Isolation
14.3.2 Joint Power Management of System Components
14.3.3 Holistic System-Level Power Management
14.4 Autonomic Power and Performance Optimization of Memory Subsystems in Server Platforms
14.4.1 Adaptive Memory Interleaving Technique for Power and Performance Management
14.4.1.1 Formulating the optimization problem
14.4.1.2 Memory appflow
14.4.2 Industry Techniques
14.4.2.1 Enhancements in memory hardware design
14.4.2.2 Adding more operating states
14.4.2.3 Faster transition to and from low power states
14.4.2.4 Memory consolidation
14.5 Conclusion
References
15 ROD: A PRACTICAL APPROACH TO IMPROVING RELIABILITY OF ENERGY-EFFICIENT PARALLEL DISK SYSTEMS
15.1 Introduction
15.2 Modeling Reliability of Energy-Efficient Parallel Disks
15.2.1 The MINT Model
15.2.1.1 Disk utilization
15.2.1.2 Temperature
15.2.1.3 Power-state transition frequency
15.2.1.4 Single disk reliability model
15.2.2 MAID, Massive Arrays of Idle Disks
15.3 Improving Reliability of MAID via Disk Swapping
15.3.1 Improving Reliability of Cache Disks in MAID
15.3.2 Swapping Disks Multiple Times
15.4 Experimental Results and Evaluation
15.4.1 Experimental Setup
15.4.2 Disk Utilization
15.4.3 The Single Disk Swapping Strategy
15.4.4 The Multiple Disk Swapping Strategy
15.5 Related Work
15.6 Conclusions
References
16 EMBRACING THE MEMORY AND I/O WALLS FOR ENERGY-EFFICIENT SCIENTIFIC COMPUTING
16.1 Introduction
16.2 Background and Related Work
16.2.1 DVFS-Enabled Processors
16.2.2 DVFS Scheduling Algorithms
16.2.3 Memory-Aware, Interval-Based Algorithms
16.3 b-Adaptation: A New DVFS Algorithm
16.3.1 The Compute-Boundedness Metric, b
16.3.2 The Frequency Calculating Formula, f*
16.3.3 The Online b Estimation
16.3.4 Putting It All Together
16.4 Algorithm Effectiveness
16.4.1 A Comparison to Other DVFS Algorithms
16.4.2 Frequency Emulation
16.4.3 The Minimum Dependence to the PMU
16.5 Conclusions and Future Work
References
17 MULTIPLE FREQUENCY SELECTION IN DVFS-ENABLED PROCESSORS TO MINIMIZE ENERGY CONSUMPTION
17.1 Introduction
17.2 Energy Efficiency in HPC Systems
17.3 Exploitation of Dynamic Voltage-Frequency Scaling
17.3.1 Independent Slack Reclamation
17.3.2 Integrated Schedule Generation
17.4 Preliminaries
17.4.1 System and Application Models
17.4.2 Energy Model
17.5 Energy-Aware Scheduling via DVFS
17.5.1 Optimum Continuous Frequency
17.5.2 Reference Dynamic Voltage-Frequency Scaling (RDVFS)
17.5.3 Maximum-Minimum-Frequency for Dynamic Voltage-Frequency Scaling (MMF-DVFS)
17.5.4 Multiple Frequency Selection for Dynamic Voltage-Frequency Scaling (MFS-DVFS)
17.5.4.1 Task eligibility
17.6 Experimental Results
17.6.1 Simulation Settings
17.6.2 Results
17.7 Conclusion
References
18 THE PARAMOUNTCY OF RECONFIGURABLE COMPUTING
18.1 Introduction
18.2 Why Computers are Important
18.2.1 Computing for a Sustainable Environment
18.3 Performance Progress Stalled
18.3.1 Unaffordable Energy Consumption of Computing
18.3.2 Crashing into the Programming Wall
18.4 The Tail is Wagging the Dog (Accelerators)
18.4.1 Hardwired Accelerators
18.4.2 Programmable Accelerators
18.5 Reconfigurable Computing
18.5.1 Speedup Factors by FPGAs
18.5.2 The Reconfigurable Computing Paradox
18.5.3 Saving Energy by Reconfigurable Computing
18.5.3.1 Traditional green computing
18.5.3.2 The role of graphics processors
18.5.3.3 Wintel versus ARM
18.5.4 Reconfigurable Computing is the Silver Bullet
18.5.4.1 A new world model of computing
18.5.5 The Twin-Paradigm Approach to Tear Down the Wall
18.5.6 A Mass Movement Needed as Soon as Possible
18.5.6.1 Legacy software from the mainframe age
18.5.7 How to Reinvent Computing
18.6 Conclusions
List of Abbreviations
References
19 WORKLOAD CLUSTERING FOR INCREASING ENERGY SAVINGS ON EMBEDDED MPSOCS
19.1 Introduction
19.2 Embedded MPSoC Architecture, Execution Model, and Related Work
19.3 Our Approach
19.3.1 Overview
19.3.2 Technical Details and Problem Formulation
19.3.2.1 System and job model
19.3.2.2 Mathematical programing model
19.3.2.3 Example
19.4 Experimental Evaluation
19.5 Conclusions
References
20 ENERGY-EFFICIENT INTERNET INFRASTRUCTURE
20.1 Introduction
20.1.1 Performance Challenges
20.1.2 Existing Packet Forwarding Approaches
20.1.2.1 Software approaches
20.1.2.2 Hardware approaches
20.2 SRAM-Based Pipelined IP Lookup Architectures: Alternative to TCAMs
20.3 Data Structure Optimization for Power Efficiency
20.3.1 Problem Formulation
20.3.1.1 Non-pipelined and pipelined engines
20.3.1.2 Power function of SRAM
20.3.2 Special Case: Uniform Stride
20.3.3 Dynamic Programming
20.3.4 Performance Evaluation
20.3.4.1 Results for non-pipelined architecture
20.3.4.2 Results for pipelined architecture
20.4 Architectural Optimization to Reduce Dynamic Power Dissipation
20.4.1 Analysis and Motivation
20.4.1.1 Traffic locality
20.4.1.2 Traffic rate variation
20.4.1.3 Access frequency on different stages
20.4.2 Architecture-Specific Techniques
20.4.2.1 Inherent caching
20.4.2.2 Local clocking
20.4.2.3 Fine-grained memory enabling
20.4.3 Performance Evaluation
20.5 Related Work
20.6 Summary
References
21 DEMAND RESPONSE IN THE SMART GRID: A DISTRIBUTED COMPUTING PERSPECTIVE
21.1 Introduction
21.2 Demand Response
21.2.1 Existing Demand Response Programs
21.2.2 Demand Response Supported by the Smart Grid
21.3 Demand Response as a Distributed System
21.3.1 An Overlay Network for Demand Response
21.3.2 Event Driven Demand Response
21.3.3 Cost Driven Demand Response
21.3.4 A Decentralized Demand Response Framework
21.3.5 Accountability of Coordination Decision Making
21.4 Summary
References
22 RESOURCE MANAGEMENT FOR DISTRIBUTED MOBILE COMPUTING
22.1 Introduction
22.2 Single-Hop Energy-Constrained Environment
22.2.1 System Model
22.2.2 Related Work
22.2.3 Heuristic Descriptions
22.2.3.1 Mapping event
22.2.3.2 Scheduling communications
22.2.3.3 Opportunistic load balancing and minimum energy greedy heuristics
22.2.3.4 ME-MC heuristic
22.2.3.5 ME-ME heuristic
22.2.3.6 CRME heuristic
22.2.3.7 Originator and random
22.2.3.8 Upper bound
22.2.4 Simulation Model
22.2.5 Results
22.2.6 Summary
22.3 Multihop Distributed Mobile Computing Environment
22.3.1 The Multihop System Model
22.3.2 Energy-Aware Routing Protocol
22.3.2.1 Overview
22.3.2.2 DSDV
22.3.2.3 DSDV remaining energy
22.3.2.4 DSDV-energy consumption per remaining energy
22.3.3 Heuristic Description
22.3.3.1 Random
22.3.3.2 Estimated minimum total energy (EMTE)
22.3.3.3 K-percent-speed (KPS) and K-percent-energy (KPE)
22.3.3.4 Energy ratio and distance (ERD)
22.3.3.5 ETC and distance (ETCD)
22.3.3.6 Minimum execution time (MET)
22.3.3.7 Minimum completion time (MCT) and minimum completion time with DVS (MCT-DVS)
22.3.3.8 Switching algorithm (SA)
22.3.4 Simulation Model
22.3.5 Results
22.3.5.1 Distributed resource management
22.3.5.2 Energy-aware protocol
22.3.6 Summary
22.4 Future Work
References
23 AN ENERGY-AWARE FRAMEWORK FOR MOBILE DATA MINING
23.1 Introduction
23.2 System Architecture
23.3 Mobile Device Components
23.4 Energy Model
23.5 Clustering Scheme
23.5.1 Clustering the M2M Architecture
23.6 Conclusion
References
24 ENERGY AWARENESS AND EFFICIENCY IN WIRELESS SENSOR NETWORKS: FROM PHYSICAL DEVICES TO THE COMMUNICATION LINK
24.1 Introduction
24.2 WSN and Power Dissipation Models
24.2.1 Network and Node Architecture
24.2.2 Sources of Power Dissipation in WSNs
24.3 Strategies for Energy Optimization
24.3.1 Intranode Level
24.3.1.1 Duty cycling
24.3.1.2 Adaptive sensing
24.3.1.3 Dynamic voltage scale (DVS)
24.3.1.4 OS task scheduling
24.3.2 Internode Level
24.3.2.1 Transmission power control
24.3.2.2 Dynamic modulation scaling
24.3.2.3 Link layer optimizations
24.4 Final Remarks
References
25 NETWORK-WIDE STRATEGIES FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS
25.1 Introduction
25.2 Data Link Layer
25.2.1 Topology Control Protocols
25.2.2 Energy-Efficient MAC Protocols
25.2.2.1 Scheduled MAC protocols in WSNs
25.2.2.2 Contention-based MAC protocols
25.3 Network Layer
25.3.1 Flat and Hierarchical Protocols
25.4 Transport Layer
25.5 Application Layer
25.5.1 Task Scheduling
25.5.2 Data Aggregation and Data Fusion in WSNs
25.5.2.1 Approaches of data fusion for energy efficiency
25.5.2.2 Data aggregation strategies
25.6 Final Remarks
References
26 ENERGY MANAGEMENT IN HETEROGENEOUS WIRELESS HEALTH CARE NETWORKS
26.1 Introduction
26.2 System Model
26.2.1 Health Monitoring Task Model
26.3 Collaborative Distributed Environmental Sensing
26.3.1 Node Neighborhood and Localization Rate
26.3.2 Energy Ratio and Sensing Rate
26.3.3 Duty Cycling and Prediction
26.4 Task Assignment in a Body Area Network
26.4.1 Optimal Task Assignment
26.4.2 Dynamic Task Assignment
26.4.2.1 DynAGreen algorithm
26.4.2.2 DynAGreenLife algorithm
26.5 Results
26.5.1 Collaborative Sensing
26.5.1.1 Results
26.5.2 Dynamic Task Assignment
26.5.2.1 Performance in static conditions
26.5.2.2 Dynamic adaptability
26.6 Conclusion
References
INDEX
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