Resource Management on Distributed Systems

Name: Resource Management on Distributed Systems | Principles and Techniques
Brand: Wiley-Blackwell
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Principles and Techniques

Shikharesh Majumdar(Autor*in)

Wiley-Blackwell (Verlag)

1. Auflage

Erschienen am 4. September 2024

323 Seiten

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978-1-119-91294-1 (ISBN)

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Inhalt

Cover
Title Page
Copyright
Contents
About the Author
Preface
Acknowledgments
Chapter 1 Introduction
1.1 Introduction to Distributed and Parallel Computing
1.2 Types of Computing Environments
1.3 Units of Computation
1.3.1 Process
1.3.2 Threads
1.3.3 Resource Management Operations
1.4 Principles Underlying Resource Management
1.4.1 Principle 1: Use Knowledge of Application/Workload Characteristics
1.4.2 Principle 2: Monitor and Adjust
1.4.3 Principle 3: Use Knowledge of System Characteristics
1.4.4 Principle 4: Perform Load Balancing
1.4.5 Principle 5: Static Versus Dynamic
1.5 Evolution of Distributed Systems
1.5.1 Nodes Communicating via Remote Procedure Calls (RPC)
1.5.2 Distributed Object Based Computing Systems
1.5.3 Parallel and Cluster Computing
1.5.4 Service-Oriented Architecture
1.5.5 Grids
1.5.6 Clouds
1.5.7 Edge Computing
1.5.8 Smart Facilities
1.6 Summary
1.6.1 Book Components
References
Chapter 2 Characterization of Parallelism in Applications
2.1 Introduction
2.2 The Precedence Graph Model
2.3 Graph-Based Characteristics
2.3.1 Parallelism Profile
2.3.2 Shape
2.4 Single-Point Characteristics
2.4.1 Maximum Parallelism
2.4.2 Fraction of Sequential Work
2.4.3 Average Parallelism
2.5 Performance Metrics
2.5.1 Completion Time
2.5.1.1 Processor Sharing Scheduling
2.5.2 Speedup
2.5.3 Efficiency
2.5.4 Completion Time-Efficiency Profile
2.5.4.1 The Knee
2.6 Impact of Parallelism Characteristics on Performance
2.6.1 Impact of Fraction of Sequential work
2.6.2 Impact of Average Parallelism
2.6.2.1 Bounds Based on Multiple Characteristics
2.7 Energy Performance Trade-Off
2.7.1 Relationship Between Energy Consumption and Speedup
2.7.2 Relationship Between Energy and Fraction of Sequential Work
2.7.3 Relationship Between Energy and Average Parallelism
2.8 Summary
Exercises
References
Chapter 3 Resource Management Techniques for Distributed Computing Systems
3.1 Resource Allocation
3.1.1 Graham's Anomaly
3.1.2 The Impact of Processor Allocation on Performance
3.1.3 Optimal Allocation Techniques
3.1.3.1 Hu's Algorithm
3.1.4 Heuristic Techniques for Processor Allocation
3.1.4.1 Largest Processing Time First
3.1.4.2 The Multifit Technique
3.2 Task/Process Scheduling
3.2.1 Scheduling of Ready Tasks on a Single Processor
3.2.1.1 Scheduling Tasks with SLAs
3.2.1.2 Performance Analysis
3.3 Grid Scheduling with Deadlines
3.3.1 Performance Analysis
3.4 Scheduling on Client-Server Systems
3.4.1 Performance Analysis
3.4.2 Software Bottlenecks
3.5 Summary
Exercises
References
Chapter 4 Resource Management on Systems Subjected to Uncertainties Associated with Workload and System Parameters
4.1 Introduction
4.2 Handling Errors Associated with User Estimates of Job Execution Times
4.2.1 Overestimation of Job Execution Times
4.2.1.1 Schedule Exceptions Manager
4.2.1.2 Prescheduling Engine
4.3 Underestimation of Job Execution Times
4.3.1 Performance Analysis
4.4 Handling Uncertainties Associated with the Local Scheduling Policy
4.5 Any Schedulability Criterion
4.5.1 Application of AS Criterion: An Example
4.6 Matchmaking in the Dark: AS Criterion-Based Matchmaking
4.6.1 Hybrid Matchmaking
4.6.1.1 Independent
4.6.1.2 Combined
4.6.2 Performance Comparison of Independent and Combined Matchmaking
4.7 Soft Advance Reservation Requests
4.7.1 Computation of System-Generated Estimate of Job Execution Time
4.7.2 Performance Evaluation
4.8 Summary
Exercises
References
Chapter 5 Resource Auto-Scaling
5.1 Introduction
5.1.1 An Example System
5.2 Request Characteristics
5.3 Horizontal Auto-Scaling
5.3.1 Reactive Auto-Scaling
5.3.1.1 Reactive Auto-Scaling Algorithm
5.3.2 Performance Analysis of the Reactive Auto-Scaling Algorithm
5.3.3 Proactive Auto-Scaling
5.3.3.1 The Proactive Auto-Scaling Algorithm
5.3.4 Performance Analysis of the Proactive Auto-Scaling Algorithm
5.4 Hybrid Auto-Scaling
5.4.1 Performance Analysis of the Hybrid Auto-Scaling Algorithm
5.4.2 Comparison with Pure Reactive and Proactive Approaches
5.5 Summary
Exercises
References
Chapter 6 Resource Management for Systems Running MapReduce Jobs
6.1 Introduction
6.2 MapReduce
6.3 Resource Management Techniques for MapReduce Job Requests to be Satisfied on a Best Effort Basis
6.4 Resource Management Techniques for MapReduce Job Requests with Service Level Agreements
6.4.1 The Budget-Based MapReduce Resource Management Technique
6.4.1.1 High-Level Description
6.4.2 Performance Analysis
6.5 The Constraint Programming-Based MapReduce Resource Management Technique
6.5.1 The MRCP-RM Algorithm
6.5.2 Performance Analysis for MRCP-RM
6.6 Errors Associated with User Estimates of Task Execution times
6.6.1 The Prescheduling Error-Handling Technique
6.6.1.1 Performance Analysis
6.6.2 The Runtime Error-Handling Technique
6.6.2.1 Performance Analysis
6.7 Summary
Exercises
References
Chapter 7 Energy Aware Resource Management
7.1 Introduction
7.1.1 Dynamic Voltage Frequency Scaling
7.2 DVFS-Based Resource Management Techniques
7.3 The EAMR-RM Algorithm
7.3.1 Performance Analysis of EAMR-RM
7.3.1.1 The Impact of Arrival Rate of Jobs
7.3.1.2 The Effect of Map Task Execution Times
7.3.1.3 The Impact of Number of Resources
7.4 Configurable Resource Manager for Processing a Batch of MapReduce Jobs
7.4.1 Constraint Program for the Configurable Resource Manager
7.5 Performance Analysis of CRM
7.5.1 Effect of Batch Completion Time Bound
7.5.2 Impact of the Missed Deadline Ratio Bound
7.5.3 The Impact of Deadline Multiplier
7.6 Reducing the Number of Active Servers
7.7 Summary
Exercises
References
Chapter 8 Streaming Data and Complex Event Processing
8.1 Introduction
8.2 Management of Streaming Data
8.3 Dynamic Priority-Based Scheduling
8.3.1 The Spark Streaming Data Processing Platform
8.3.1.1 The Spark Streaming System
8.4 Data-Driven Priority Scheduler (DDPS)
8.4.1 Algorithm 1
8.4.2 Algorithm 2
8.4.3 Algorithm 3
8.4.4 Performance Analysis
8.5 Multitennant Systems
8.5.1 Apache Storm
8.5.2 Resource Management on a Multitenant Storm Cluster
8.5.2.1 Isolation Scheduler
8.5.2.2 Static Priority-Based Scheduler (SPS)
8.5.2.3 Dynamic Priority Scheduler
8.5.2.4 Performance Analysis
8.6 Complex Event Processing
8.6.1 CQL Query and CEP
8.6.2 CEP Architecture
8.6.3 Example CEP Systems
8.6.4 CEP Platforms
8.6.4.1 Apache Siddhi
8.7 Remote Patient Monitoring System
8.7.1 The SCEP System
8.7.2 The Mobile CEP (MCEP) System
8.7.3 Performance Analysis
8.7.3.1 Remaining Battery Life
8.7.3.2 CEP Latency
8.8 Summary
Exercises
References
Chapter 9 Data Indexing and Filtering Techniques for Big Data Systems
9.1 Introduction
9.2 Harnessing Big Data
9.3 Data Indexing
9.4 Inverted Index
9.4.1 Enhancements to the Inverted Index Technique
9.5 Graph-Based Indexing
9.6 Boolean AND Queries
9.7 Performance Analysis
9.7.1 Search Latency
9.7.1.1 The Impact of Dcount on Search Latency
9.7.1.2 Impact of SKcount on Search Latency
9.7.2 Indexing Overhead
9.7.2.1 Indexing Time
9.7.2.2 Memory Usage
9.8 Data Filtering
9.8.1 Processing of Large Volumes of Data
9.9 Parallel Processing Platforms
9.9.1 Spark Architecture
9.10 Motivations for Data Reduction
9.10.1 Reducing Data Volume
9.11 Data Filtering
9.11.1 Basic Approach for Data Filtering
9.11.2 The Filtering Algorithm
9.11.3 Search Method
9.12 Performance Analysis
9.12.1 Node Parallelism vs. Core Parallelism
9.13 Streaming Data
9.13.1 System Performance
9.14 Handling User Preferences Comprising Keywords Connected by Boolean Operators
9.14.1 Performance Analysis
9.15 Summary
Exercises
References
Chapter 10 Sensor-Based Systems
10.1 Introduction
10.1.1 Architecture for Cloud-Based Smart Facilities Management
10.2 Middleware Services
10.3 Sensor-Based Bridge Management
10.4 Research Collaboration Platform for Management of Sensor-Based Smart Facilities
10.5 Resource Management on Wireless Sensor Networks
10.6 Scheduling on WSNs
10.6.1 Knowledge Free Algorithms
10.6.2 Knowledge-Based Algorithms
10.6.2.1 Description of the Knowledge-Based Scheduling Algorithms
10.6.3 Performance of Scheduling Algorithms
10.6.3.1 Performance of Algorithms That Use a Single Characteristic
10.6.3.2 Performance of Algorithms That Use the Knowledge of Both Application and System Characteristics
10.7 Sensor Allocation
10.7.1 Dynamic Allocation Algorithms
10.7.1.1 Knowledge-Free Algorithm
10.7.1.2 Knowledge-Based Algorithms
10.7.2 Static Allocation Algorithms
10.7.2.1 Knowledge-Free Allocation Algorithms
10.7.2.2 Allocation Algorithms Based on the Knowledge of Applications
10.7.3 Performance of Sensor Allocation Algorithms
10.7.3.1 Performance of Dynamic Allocation Algorithms
10.7.3.2 Performance of Static Allocation Algorithms
10.8 Summary
Exercises
References
Chapter 11 Summary
11.1 Chapter Entitled Introduction
11.1.1 Types of Computing Environments
11.1.2 Units of Computation: Processes and Threads
11.1.3 Resource Management
11.1.4 Various Forms of Distributed Systems
11.2 Chapter Entitled Characterization of Parallelism in Applications
11.2.1 Graph-Based Characteristics
11.2.2 Single-Point Characteristics
11.2.3 Performance Measures
11.3 Chapter Entitled Resource Management Techniques for Distributed Computing Systems
11.4 Chapter Entitled Resource Management on Systems Subjected to Uncertainties Associated with Workload and System Parameters
11.4.1 Handing Errors Associated with User Estimates of Task Execution Times
11.4.2 Handling Uncertainties Associated with the Local Scheduling Policy
11.5 Chapter Entitled Resource Auto-Scaling
11.6 Chapter Entitled Resource Management on Systems Running MapReduce Jobs
11.7 Chapter Entitled Energy-Aware Resource Management
11.8 Chapter Entitled Streaming Data and Complex Event Processing
11.8.1 Resource Management
11.8.2 Complex Event Processing
11.9 Chapter Entitled Data Indexing and Filtering Techniques for Big Data Systems
11.9.1 Data Indexing
11.9.2 Data Filtering
11.10 Chapter Entitled Sensor-Based Systems
11.10.1 Smart Facilities Management
11.10.2 Management of Wireless Sensor Networks
11.10.2.1 Scheduling of Requests
11.10.2.2 Sensor Allocation
Index
EULA

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