Table Of ContentVIRTUAL COMPUTING
Concept, Design, and Evaluation
THE KLUWER INTERNATIONAL SERIES
IN ENGINEERING AND COMPUTER SCIENCE
VIRTUAL COMPUTING
Concept, Design, and Evaluation
by
Dongmin Kim
Wide Area Network Design Laboratory (WANDL),
Boundbrook, NJ
Salim Hariri
The University of Arizona
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
ISBN 978-1-4613-5611-0 ISBN 978-1-4615-1553-1 (eBook)
DOI 10.1007/978-1-4615-1553-1
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright © 2001 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 2001
Softeover reprint of the hardcover 1st edition 2001
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, mechanical, photo
copying, recording, or otherwise, without the prior written permission of the
publisher.
Printed on acid-free paper.
Contents
List of Figures ix
List of Tables xi
Preface xiii
Contributing Authors xix
1
Trends in Parallel and Distributed Computing 1
1.1 Trends in Processing Technology 2
1.1.1 Dimension I - Advances in device technology 3
1.1.2 Dimension II - Advances in concurrency technology 4
1.2 Trends in Computer Networking Technology 4
1.2.1 Network Component 4
1.2.2 Transport Component 6
1.3 Trends in Software Tools and Environments 8
2
Parallel and Distributed Computing Environment 13
2.1 Legion 13
2.1.1 Legion Objectives 13
2.1.2 Architecture 14
2.2 Globus 15
2.2.1 Globus Objectives 15
2.2.2 ArchUtecture 16
2.3 Condor 18
2.3.1 Condor Objectives 20
2.3.2 Architecture 20
2.4 Charlotte 22
2.4.1 Charlotte Objectives 22
2.4.2 Architecture 22
3
Trends Toward Virtual Computing 25
3.1 Application Computing Requirements 25
3.1.1 Application Classes 25
vi VIRTUAL COMPUTING: CONCEPT, DESIGN, AND EVALUATION
3.1.2 Characteristics of Representative Algorithms 27
3.1.3 Parallelism Unit 27
3.1.4 Computational Scalability 27
3.1.5 Interconnection Requirement 29
3.1.6 Control Requirement 29
3.1.7 Natural Support Architecture 29
3.2 Communication Protocol Requirements 30
3.2.1 Simplicity 30
3.2.2 Low latency and high bandwidth 31
3.2.3 Concurrent Interprocess Communication 31
3.2.4 Multicasting 31
3.2.5 Support for Process Synchronization 32
3.2.6 Efficient Flow Control Scheme 32
4
Adaptive Distributed Virtual Computing Environment 35
4.1 Overview of ADViCE Architecture 35
4.2 Visualization and Editing Services (VES) 37
4.2.1 Application Editing Service (AES) 37
4.2.2 Application Visualization Service (AVS) 39
4.3 ADViCE Server 40
4.3.1 Initial and Runtime Mapping Service (IRS) 41
4.3.2 Application Management Service (AMS) 41
4.3.3 Application Control Service (ACS) 43
4.3.4 Application Data Service (ADS) 44
4.4 ADViCE Adaptation Approach 45
5
Resource Allocation and Scheduling 53
5.1 Overview of current scheduling algorithms 53
5.1.1 Dynamic Level Scheduling 55
5.1.2 Levelized Min Time 55
5.1.3 Mapping Heuristic 56
5.1.4 Heterogeneous Earliest Finish Time 56
5.2 Definitions 57
5.2.1 Definitions of Terms 57
5.2.2 Definition of Task Scheduling 59
5.3 Architecture of a Task Schedule 59
5.3.1 Initial Mapping Service 59
5.3.2 Runtime Mapping Service 60
6
Analysis and Prediction of Task Execution 61
6.1 Definitions and Notations 61
6.2 Assumptions 62
6.3 CPU Allocated Time - tcpu(Ti, Tj, 0, t) 63
6.4 Task Execution Time Prediction - trem (Ti' T j, t) 67
6.4.1 Future Load Prediction 67
6.5 Tuning Function - ttun(~, Tj, tbeg, tend) 70
Contents vii
6.5.1 Load Estimation Errors 70
6.5.2 Tuning Function Example 73
7
Task-Scheduling Algorithm 79
7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 79
7.1.1 Task Prioritizing Phase 79
7.1.2 Processor Selection Phase 80
7.1.3 Complexity Analysis of the HEFT Algorithm 80
7.1.4 HEFT Algorithm Drawbacks 81
7.2 Dynamic Heterogeneous Prediction-Based Scheduling Algorithm
(DHPB) 82
7.2.1 The DHPB Algorithm for Initial Mapping Service 83
7.2.2 The DHPB Algorithm for Runtime Mapping Service 83
7.2.3 Complexity of DHPB Algorithm 86
7.2.3.1 DHPB for Initial Mapping 86
7.2.3.2 DHPB for Runtime Mapping 86
8
Experimental Results and Analysis 87
8.1 Experiment 1: Using ADViCE as a Parallel Evaluation Tool 87
8.2 Experiment 2: Using ADViCE as a Problem Solving Environ-
ment 92
8.3 Experiment 3: Evaluation of the ADViCE Adaptation Ap-
proach 93
8.4 Experiment 4: Prediction of Task Execution Time 96
8.5 Task-Scheduling Environment 101
8.6 Experiment 5: Randomly Generated Application Flow Graph 102
8.7 Experiment 6: Gaussian Elimination 105
9
Summary and Future Directions 109
9.1 Summary 109
9.2 Future Work 110
9.3 Future Trends 111
References 113
Index 123
List of Figures
1.1 Trends in network technology 5
2.1 Legion Programming Model 14
2.2 The Architecture of Globus 19
2.3 Architecture of Condor Pool 21
2.4 Charlotte's Programming/Execution Model 23
4.1 Adaptive Changes in the ADViCE Environment 36
4.2 Main Components of ADViCE Architecture 38
4.3 Application Flow Graph 39
4.4 Visualization of ADViCE 40
4.5 Execution Flow of Mapping Service 42
4.6 Adaptation Approach 46
4.7 ADViCE Adaptation Algorithm 48
4.8 ADViCE Change Detection Procedures 49
4.9 Verification and Analysis Procedures of AES 50
4.10 Verification and Analysis Procedures of ADS 50
4.11 Adaptation Plan Procedures 51
5.1 Classification of Task-Scheduling Algorithm 54
5.2 Application Flow Graph for Solving the Linear Equation 58
5.3 Example of Application Flow Graph 58
5.4 Architecture of Task Scheduling 60
6.1 The Example of Load Trace on Resource ri 62
6.2 Collection of Resource and Task Information 63
6.3 Execution Time of LV-Decomposition, Inverse Ma-
trix and Matrix Multiplication 64
6.4 Example of Running Two Tasks on a Resource 66
6.5 Example of Running n Tasks on a Resource 68
6.6 Load Measure Error on Task Invocation 70
6.7 Load Measure Error Affected by Another Task's Invocation 71
x VIRTUAL COMPUTING: CONCEPT, DESIGN, AND EVALUATION
6.8 Load Measure Error Affected by Another Task's
Termination 72
6.9 Example of the Execution - LU and INV 73
7.1 The HEFT Algorithm 80
7.2 Sample Task Graph with Ten Tasks 81
7.3 Computation Cost Change Due to Load 82
7.4 Mapping Changes 83
7.5 The DHPB Algorithm for Initial Mapping Service 84
7.6 The DHPB Algorithm for Runtime Mapping Service 85
8.1 The Configuration of the Current ADViCE Testbed 88
8.2 The Performance of the ADViCE Implementation
of the Matrix-Vector Multiplication 89
8.3 The Performance of the P4 Implementation of the
Matrix-Vector Multiplication 90
8.4 An Example of Fault-Tolerant Distributed Application 94
8.5 An Example of the ADViCE Adaptation Algorithm 95
8.6 The Invocation of Tasks 96
8.7 The Load Trace to IS-Minute Point 97
8.8 Distribution Graph of Prediction Error 102
8.9 Execution of Randomly Generated AFG 103
8.10 Comparison of Total Execution Time for Randomly
Generated AFGs 104
8.11 Comparison of Speedup for Randomly Generated AFGs 105
8.12 Gaussian Elimination Sequential Algorithm 106
8.13 Tasks of the Gaussian Elimination Algorithm 106
8.14 Application Flow Graph of the Gaussian Elimina-
tion Algorithm 107
8.15 Comparison of Total Execution Time for Gaussian
Elimination 107
8.16 Comparison of Speedup for Gaussian Elimination 108
9.1 Architectural trends towards Virtual Computing En-
vironment 112
List of Tables
2.1 Core Globus Services 17
3.1 Classification of Real-World Applications 26
3.2 Requirement Characterization of Application Classes 28
6.1 Comparison of Real and Predicted Remaining Exe-
cution Times 75
6.2 Comparison of Real and Predicted Execution Times
With/Without Tuning 77
7.1 HEFT Mapping Result for Graph in Figure 7.2 81
7.2 Mapping Result for Graph in Figure 7.2; Applying
the Computation Cost Change in Figure 7.3 82
7.3 DHPB Runtime Mapping Result for the Application
Graph in Figure 7.2 86
8.1 Performance Comparison of the Matrix-Vector Mul-
tiplication Task for Each Software Development Phase 91
8.2 Performance Comparison of the Linear Equation
Solver Application for Each Software Development Phase 92
8.3 The List of Tasks used in Prediction Experiment 96
8.4 Intermediate Result for t without tuning function 98
rem
8.5 Prediction of Execution Without Tuning Function 99
8.6 Intermediate Result for t With Tuning Function 100
rem
8.7 Prediction of Execution With Tuning Function 101
8.8 Comparison of Real and Predicted Execution Time
With/Without Tuning 101
8.9 The List of Tasks Used in Randomly Generated AFG 104
