Large scale network-centric distributed systems:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2014
|
| Schriftenreihe: | Wiley series on parallel and distributed computing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | LVIII, 700 S. Ill., graf. Darst. |
| ISBN: | 9780470936887 |
Internformat
MARC
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| 245 | 1 | 0 | |a Large scale network-centric distributed systems |c ed. by Hamid Sarbazi-Azad ... |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2014 | |
| 300 | |a LVIII, 700 S. |b Ill., graf. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series on parallel and distributed computing | |
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| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026855145&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-026855145 | ||
Datensatz im Suchindex
| _version_ | 1804151517037461504 |
|---|---|
| adam_text | CONTENTS
Preface
xxix
Acknowledgments
xxxvii
List of Figures
xxxix
List of Tables
li
List of Contributors
lv
PART
1
MULTICORE AND MANY-CORE
(MC) SYSTEMS-ON-CHIP
1
A RECONFIGURABLE ON-CHIP INTERCONNECTION NETWORK
FOR LARGE MULTICORE SYSTEMS
3
Mehdi Modarressi and
Hamid
Sarbazi-Azad
1.1
Introduction
4
1.1.1
Multicore and Many-Core Era
4
1.1.2
On-Chip Communication
4
1.1.3
Conventional Communication Mechanisms
4
1.1.4
Network-on-Chip
5
1.1.5
NoC Topology Customization
6
1.1.6
NoCs and Topology Reconfigurations
6
1.1.7
Reconfigurations Policy
7
1.2
Topology and Reconfiguration
8
1.3
The Proposed NoC Architecture
9
1.3.1
Baseline
Reconfigurable
NoC
9
1.3.2
Generalized
Reconfigurable
NoC
13
1.4
Energy and Performance-Aware Mapping
14
1.4.1
The Design Procedure for the Baseline
Reconfigurable NoC
14
1.4.1.1
Core-to-Network Mapping
15
1.4.1.2
Topology and Route Generation
16
1.4.2
Mapping and Topology Generation for Cluster-Based NoC
18
VII
VIU
CONTENTS
1.5
Experimental Results
19
1.5.1
Baseline
Reconfigurable NoC
21
1.5.2
Performance Evaluation with Cost Constraints
22
1.5.3
Comparison Cluster-Based NoC
23
1.6
Conclusion
25
References
25
COMPILERS, TECHNIQUES, AND TOOLS FOR SUPPORTING
PROGRAMMING HETEROGENEOUS MANY/MULTICORE
SYSTEMS
31
Pasquale
Canlielto,
Beniamino Di Martino,
atui
Francesco
Moscato
2.
1 Introduction
32
2.2
Programming Models and Tools for Many/Multicore
32
2.2.
1 OpenMP
33
2.2.2
Brook for GPUs
34
2.2.3
Sh
35
2.2.4
CUDA
36
2.2.4.
1 Memory Management
36
2.2.4.2
Kernel Creation and Invocation
37
2.2.4.3
Synchronization
38
2.2.5
HMPP
38
2.2.6
OpenCL
39
2.2.7
OpenAcc
41
2.3
Compilers and Support Tools
42
2.3.1
RapidMind Multicore Development Platform
42
2.3.2
OpenMPC
43
2.3.3
Source-to-Source Transformers
43
2.3.3.1
CHiLL
44
2.3.3.2
Cetus
44
2.3.3.3
ROSE Compiler
45
2.3.3.4
LLVM
45
2.4
CALuMET: A Tool for Supporting Software Parallelization
45
2.4.1
Component-Based Source Code Analysis Architecture
45
2.4.2
Algorithmic Recognizer Add-on
47
2.4.3
Source Code Transformer for GPUs
48
2.5
Conclusion
49
References
50
CONTENTS
IX
A MULTITHREADED BRANCH-AND-BOUND ALGORITHM FOR
SOLVING THE FLOW-SHOP PROBLEM ON A MULTICORE
ENVIRONMENT
53
Mohand Mezmaz, Nouredine Melah, and Daniel Tuyttens
3.1
Introduction
54
3.2
Flow-Shop Scheduling Problem
55
3.3
Parallel Branch-and-Bound Algorithms
56
3.3.1
Multiparametric Parallel Model
56
3.3.2
Parallel Tree Exploration Model
57
3.3.3
Parallel Evaluation of Bounds
57
3.3.4
Parallel Evaluation of a Bound Model
58
3.4
A Multithreaded Branch-and-Bound
58
3.4.1
Low-Level Multithreaded B&B
58
3.4.2
High-Level Multithreaded B&B
59
3.5
The Proposed Multithreaded B&B
60
3.6
Experiments and Results
63
3.6.1
Flow-Shop Instances
63
3.6.2
Hardware and Software
Testbed
64
3.6.3
Experimental Protocol
64
3.6.4
Performance Analysis
65
3.6.5
Page Faults
66
3.6.6
Context Switches
68
3.7
Conclusion
68
References
70
PART
2
PERVASIVE/UBIQUITOUS COMPUTING AND PEER-TO-PEER
SYSTEMS
4
LARGE-SCALE PZP-INSPIRED PROBLEM-SOLVING: A FORMAL
AND EXPERIMENTAL STUDY
73
Mathieu Djamaï,
Bilei
Oerbel,
and Nouredine Melab
4.1
Introduction
74
4.1.1
Motivations
74
4.1.2
Contribution and Results
75
4.1.3
Related Works
76
4.1.4
Outline
77
4.2
Background
77
4.3
A Pure Peer-to-Peer B&B Approach
80
CONTENTS
4.3.1
Preliminaries
80
4.3.2 Information
Sharing and Work Distribution HI
4.3.2.1
Best Solution Sharing Mechanism
82
4.3.2.2
Work Sharing
82
4.3.2.3
Load Balancing
83
4.3.3
Distributed Termination Detection
S3
4.3.3.1
Basic Observations
83
4.3.3.2
Technical Details Sketch
85
4.3.4
Asynchrony Issues
86
4.4
Complexity Issues
87
4.5
I
ix
peri men
t
al
Results
90
4.5.1
Experimental
Testbed
90
4.5.2 Experiments
on Large-Scale Networks
92
4.5.2.1
Parallel Efficiency
92
4.5.2.2
Network Congestion and Simulation Scenarios
93
4.5.2.3
Message Overhead
95
4.5.2.4
Search Space Exploration Speed-Up
96
4.5.2.5
Combinatorial Speed-up
97
4.5.3
Lower Scales Results
98
4.6
Conclusion
99
Acknowledgment
99
References
100
DATA DISTRIBUTION MANAGEMENT
103
Azzedine Boukerche and Yunfeng Gu
5.
1 Addressing DDM in Different Network Environments
104
5.2
DDM in P2P Overlay Networks
106
5.2.1
Background
106
5.2.2
Data Space Partitioning and Mapping
108
5.2.3
Corresponding Overlay Network Support
109
5.3
DDM in Cluster-Based Network Environments
111
5.3.1
Basic Concepts in DDM
111
5.3.1.1
Routing Space
112
5.3.1.2
Objects, Federates, and Federation
112
5.3.1.3
Behavior of Entities
112
5.3.1.4
Other Important Concepts
113
5.3.1.5
Performance of DDM Implementations
114
5.3.2
Background
115
CONTENTS
XI
5.3.3 Data Distribution Management
Schemes
115
5.3.3.1
Region-Based DDM Approach
115
5.3.3.2
Grid-Based DDM Approaches
116
5.3.3.3
Other DDM Schemes
117
References
118
6
MIDDLEWARE SUPPORT FOR CONTEXT HANDLING AND
INTEGRATION IN UBIQUITOUS COMPUTING
123
Frederico
Lopes, Paulo
F Pires,
Flúvia
С.
Delicato,
Thais
Batista,
and Luci
Pirmez
6.1
Introduction
124
6.2
Ubiquitous Computing
126
6.3
Middleware for Ubiquitous Computing
128
6.3.1
Approaches and Techniques
128
6.3.2
Existing Middleware Platforms
130
6.4
A Solution to Integrating Context Provision Middleware for
Ubiquitous Computing
133
6.4.1
Overview
133
6.4.2
Terminology
134
6.4.2.1
Services
134
6.4.2.2
Semantic Workflow
134
6.4.2.3
Execution Plan
135
6.4.3
Context Model
135
6.4.4
Architecture
136
6.4.5
Service Metadata
139
6.4.6
Workflow Specification
140
6.4.7
Execution Plan Selection
140
6.5
Conclusion
142
Acknowledgments
J
42
References
143
PART
3
WIRELESS/MOBILE NETWORKS
7
CHALLENGES IN THE USE OF WIRELESS SENSOR NETWORKS
FOR MONITORING THE HEALTH OF CIVIL STRUCTURES
147
Flávia
С.
Delicato,
Igor
L.
dos Santos,
Luci
Pirmez, Paulo F. Pires,
and Claudio M. de
Farias
7.
1 Introduction 1
48
XU
CONTENTS
7.2
Structural Health
Monitoring
150
7.2.1
The Concept
of
Structural Health
Monitoring
150
7.2.2
Requirements of Modal-Based Techniques in SHM Solutions
152
7.2.3
The Generations of Sensor Networks for SHM
153
7.3
Wireless Sensor Networks
J
55
7.4
Applying Wireless Sensor Networks for Structural Health Monitoring
157
7.4.1
The Second Generation of Sensor Networks for SHM
157
7.4.2
The Third Generation of Sensor Networks for SHM
158
7.4.3
A Fully Decentralized, Network-Centric Approach:
Sensor-SHM
161
7.5
Conclusion
163
Acknowledgments
164
References
164
8
MOBILITY EFFECTS IN WIRELESS MOBILE NETWORKS
167
Abbas Nay
ebi
and
Hamid
Sarbazi-Azad
8.
1 Introduction 1
67
8.2
The Effect of Node Mobility on Wireless Links 1
68
8.2.
1 Geometric Modeling 1
70
8.2.2
LL and RLL Properties 1
7
1
8.3
The Effect of Node Mobility on Network Topology 1
72
8.3.1
Definitions of Connectivity
173
8.3.2
Phase Transition Phenomenon in Connectivity and
Disconnection Degree
174
8.4
Conclusion
177
References
178
9
ANALYTICAL MODEL OF TIME-CRITICAL WIRELESS SENSOR
NETWORK: THEORY AND EVALUATION
183
Kambiz Mizanian and Amir
Hossein
Jahangir
9.1
Introduction
] 84
9.2
Real-Time Wireless Sensor Network: An Overview
185
9.2.1
Previous Work on a Related Analytical Model
185
9.2.2
Previous Work on the
Real-Time
Communication
Protocols
186
9.3
Real-Time
Degree
188
9.3.1
Basic Assumptions
188
9.3.2
Evaluation of the Real-Time Degree
190
CONTENTS
ХИІ
9.4
Reliable Real-Time Degree
195
9.5
Model Validation
197
9.6
Conclusion
199
References
200
10
MULTICAST TRANSPORT PROTOCOLS FOR LARGE-SCALE
DISTRIBUTED COLLABORATIVE ENVIRONMENTS
203
Haifa Raja Maamar and Azzedine Boukerche
10.1
Introduction
204
10.2
Definition and Features
204
10.2.1
Definition
204
10.2.2
Features
205
10.2.2.1
Reliability
205
10.2.2.2
Congestion and Flow Control
206
10.2.2.3
Ordering
206
10.2.2.4
Error Recovery
206
10.2.2.5
Group Management
207
10.3
Classification of Multicast Protocols
207
10.3.1
General-Purpose Protocols
208
10.3.1.1
Reliable Broadcast Protocol (RBP)
208
10.3.1.2
Multicast Transport Protocol (MTP)
208
10.3.1.3
Reliable Multicast Protocol (RMP)
209
10.3.1.4
Xpress Transport Protocol (XTP)
209
10.3.2
Multicast Interactive Applications
210
10.3.2.1
Multicast Transport Protocol-2 (MTP-2)
210
10.3.2.2
Real-Time
Transport Protocol (RTP)
21 1
10.3.2.3
Scalable Reliable Multicast (SRM)
211
10.3.2.4
Reliable Adaptive Multicast Protocol (RAMP)
2
1
2
10.3.3
Data Distribution Services
212
10.3.3.1
Tree-Based Multicast Transport Protocol
(TMTP)
212
10.3.3.2
Reliable Multicast Transport Protocol (RMTP)
213
10.3.3.3
Multicast File Transfer Protocol (MFTP)
215
10.3.3.4
Tree-Based Reliable Multicast Protocol
(TRAM)
215
10.4
Conclusion
216
References
216
Xiv CONTENTS
11
NATURE-INSPIRED
COMPUTING
FOR
AUTONOMIC
WIRELESS
SENSOR NETWORKS 219
Wei
U, Javiti
ľalie
>¡.
Albert
У.
/.ornava,
l-raniiszek Seredvnski,
and
lijom
I.aiidjeldi
I I
.
I Introduction
220
I 1.
2
Autonomie
WSNs
222
]
1
.3
Principles of Nature-Inspired Computing
224
I 1
.4
Cellular Automata
226
I 1
.4.
1 One-Dimensional Cellular Automata and Its
Applications in WSNs
226
I 1.
4.2
Two
Dimensionai
Cellular Automata and Its
Applications in WSNs
227
I 1
.5
Swarm Intelligence
228
I 1.
5.
1 Ant Colony Optimization
229
11.5.1.1
Basic Concepts
229
1 1.5.1.2
Applications in WSNs
230
11.5.2
Firefly Synchronization
231
1 1.5.2.1
Applications in WSNs
231
11.5.3
Particle Swarm Optimization
232
1 1.5.3.1
Applications in WSNs
233
1 1.6
Artificial Immune Systems
233
1 1.6.1
Negative Selection
233
1 1.6.2
Danger Theory
234
1 1.6.3
Clonal Selection
234
I
1.6.4
Immune Networks
235
11.6.5
Applications in WSNs
235
11.6.6
The Cognitive Immune Model
236
1 1.6.6.1
Application in WSNs
237
1
1
.7
Evolutionary Computing
238
11.7.1
Basic Concepts
238
1 1.7.2
Genetic Algorithms
239
11.7.3
Genetic Programming
239
11.7.4
Evolution Strategies
240
11.7.5
Evolutionary Programming
240
11.7.6
Applications in WSNs
241
11.8
Molecular Biology
242
11.9
Bio-Networking Architecture
243
11.10
Conclusion
244
References
244
CONTENTS
XV
PART
4
GRID AND CLOUD COMPUTING
12
SMART RPC-BASED COMPUTING IN GRIDS AND ON CLOUDS
257
Thomas Brady,
Oleg
Girko, and Alexey Lastovetsky
12.1
Introduction
258
12.1.1
GridRPC Programming Model and API
259
12.1.1.1
Design of the GridRPC Programming Model
259
12.1.1.2
GridRPC: API and Semantics
260
12.1.2
GridRPC: A GridRPC Application
261
12.1.3
Implementing the GridRPC Model in GridSolve
263
12.1.3.1
GridSolve: Agent Discovery
263
12.1.3.2
Run-time GridRPC Task Call
263
12.1.4
GridRPC Limitations
265
12.2
SmartGridRPC and SmartGridSolve
266
12.2.1
SmartGridRPC Programming Model and API
266
12.2.1.1
SmartGridRPC Programming Model
267
12.2.1.2
SmartGridRPC: API and Semantics
270
12.2.1.3
A SmartGridRPC Application
272
12.2.2
SmartGridSolve: Implementing SmartGridRPC in
GridSolve
272
12.2.2.1
Agent Discovery
274
12.2.2.2
Run-time of Client Application
274
12.2.2.3
Fault Tolerance
276
12.3
Making SmartGridSolve Smarter
277
12.3.1
SmartGridSolve Approach to Smart Mapping and Its
Limitations
277
12.3.2
A Better Approach to Smart Mapping
279
12.3.3
Better Approaches to Fault Tolerance
280
12.3.3.1
Recovery from Task Failures
280
12.3.3.2
Restarting Only Relevant Tasks
281
12.3.3.3
Losing Fewer Results
281
12.3.3.4
More Reliable Mapping
282
12.4
Smart RPC-Based Computing on Clouds: Adaptation of
SmartGridRPC and SmartGridSolve to Cloud Computing
282
12.4.1
Cloud Computing
283
12.4.1.1
Infrastructure as a Service
283
12.4.1.2
Platform as a Service
283
12.4.1.3
Software as a Service
284
XVI
CONTENTS
12.4.2
SmartCloudSolvcíSCS)
284
12.4.2.1
Overview
284
I
2.4.2.2
Advantages of the
SCS
Platform
285
12.4.2.3
High-Level Design of the
SCS
Platform
285
12.4.2.4
SCS
API and Application Implementation
286
Acknowledgment
288
References
288
13
PROFIT-MAXIMIZING RESOURCE ALLOCATION FOR
MULTITIER CLOUD COMPUTING SYSTEMS UNDER SERVICE
LEVEL AGREEMENTS
291
Hadi
(¡ondarz.i and
M
assona
Pe
d
ram
13.І
Introduction
292
1
3.2
Review of
Datacenter
Power Management Techniques
294
1
3.3
Review of
Datacenter
Performance Management Techniques
296
1
3.4
System Model of a Multitier Application Placement Problem
298
1
3.4.
1 Multitier Service Model
299
I
3.4.2
SLA Model for This System
302
1
3.4.3
Resource Management Problem
303
1
3.5
Protít
Maximization in a Hosting
Datacenter
303
1
3.5.
1 Problem Formulation
303
1
3.5.2
Initial Solution
305
13.5.3
Resource Consolidation Using Force-Directed Search
308
13.6
Simulation Results
310
13.7
Conclusion
314
References
314
14
MARKET-ORIENTED CLOUD COMPUTING AND THE
CLOUDBUS TOOLKIT
319
Rajkumar Buyya, Suraj Pandey, and Christian
Vecchiaia
1
4.1
Introduction
320
14.2
Cloud Computing
322
14.2.1
Cloud Definition and Market-Oriented Computing
323
14.2.2
Cloud Computing Reference Model
325
14.2.3
State of the Art in Cloud Computing
326
14.2.3.1
Infrastructure as a Service
326
14.2.3.2
Platform as a Service
328
CONTENTS
XVII
14.2.3.3
Software as a Service
329
14.2.3.4
Alliances
and Standardization
Initiatives
330
14.2.4
Open
Challenges
331
14.2.4.1
Virtuatization
331
14.2.4.2
Security, Privacy, and
Trust
332
14.2.4.3
Legal and Regulatory
333
14.2.4.4
Service
Level Agreements and Quality
of Service
334
14.2.4.5
Energy Efficiency
335
14.2.4.6
Programming
Environmentsand
Application Development
335
14.2.4.7
Applications on Clouds
336
14.2.4.8
Standardization
337
14.3
Cloudbus: Vision and Architecture
338
14.4
Cloudbus and Clouds Lab Technologies
340
14.4.1
Aneka
340
14.4.2
Brokers: Harnessing Cloud and Other Distributed
Resources
341
14.4.3
Workflow Engine
342
14.4.4
Market Maker/Meta-broker
343
14.4.5
InterCloud
343
14.4.6
MetaCDN
343
14.4.7
Data Center Optimization
344
14.4.8
Energy-Efficient Computing
344
14.4.9
CloudSim
344
14.5
Experimental Results
345
14.5.1
Aneka Experiment: Application Deadline-Driven
Provisioning of Cloud Resources
345
14.5.2
Broker Experiment: Scheduling on Cloud and Other
Distributed Resources
346
14.5.3
Deploying
ECG
Analysis Applications in Cloud
Using Aneka
349
14.6
Related Technologies, Integration, and Deployment
350
14.7
Conclusion
351
Acknowledgments
353
References
354
XViii
CONTENTS
15
A
CLOUD
BROKER ARCHITECTURE FOR MULTICLOUD
ENVIRONMENTS
359
Vose
Luis l.Hcds-SiiiHuro,
Ιηι χο
Sdii
Aniceto.
Rajmi
Minvnt¡- o. .nu (liinto.
Ruhen
S.
Montero.
and
¡ціннії)
M.
І.
loraiic
Iři.
I Introduction
360
1x2
Stale
ol
the Art on
(Moud
Brokering
361
15.3
Challenges ofCloud
Brokeri
n«
363
15.4
Propositi
ol
її
Broker
Architecture
Ibi Multicloud
l ini
ron
meni
s
364
15.4.1
Broker
Components
365
15.4.1.1
Database
365
15.4.1.2
Scheduler
366
15.4.1.3
VM
Manager
366
15.4.1.4
Cloud
Manager
366
15.4.2
Service
Description
366
15.4.2.1
Service
Components and Lifetime
366
15.4.2.2
Scheduling Parameters
367
15.4.2.3
Cloud Instance Usage and Instance
Performance
367
15.5
Scheduling Policies for Efficient Cloud Brokering
367
15.5.1
Static vs. Dynamic Scheduling
367
15.5.2
Optimization Criteria
367
15.5.3
User Restrictions
368
15.6
Results
369
15.6.1
Cost Optimization in a Single Cloud
369
15.6.2
Cost Optimization in a Multicloud Environment
370
15.6.2.1
Infrastructure for a Virtual Classroom
370
15.6.2.2
Infrastructure for a Web Server
371
15.7
Conclusion
373
Acknowledgments
374
References
375
16
ENERGY-EFFICIENT RESOURCE UTILIZATION IN
CLOUD COMPUTING
377
Giorgio L.
Valentini,
Samee U. Khan, and Pascal Bouvry
16.1
Introduction
378
16.2
Related Work
380
CONTENTS XIX
16.3
Energy-Efficient Utilization of Resources in Cloud
Computing Systems
38!
16.3.1
Cloud Computing
381
16.3.2
Energy Model
382
16.3.3
Task Consolidation Problem
382
16.3.4
Task Consolidation Algorithm
383
16.3.4.1
Overview
383
16.3.4.2
Cost Functions (ECTC and MaxUtil)
383
16.3.4.3
Task Consolidation Algorithm
384
16.3.5
Application of the Model: A Working Example
384
16.4
Complementarity Approach
386
16.4.1
Main Idea
386
16.4.2
Motivation
387
16.4.3
Approach
387
16.4.4
Metric Normalization
388
16.4.5
Evaluation Space
389
16.4.6
Selection of the Best Candidate
389
16.4.6.1
Mathematical Model
389
16.4.6.2
Algorithm
390
16.4.7
Processing of Equivalent Solutions
391
16.4.8
Energy-Efficient Task Consolidation Algorithm
391
16.4.9
An Intuitive Example
391
16.5
Simulation Results
395
16.5.1
Simulation Setup
395
16.5.2
Results
397
16.5.2.1
Energy Efficiency
397
16.5.2.2
Speed Analysis
402
16.6
Discussion of Results
402
16.7
Conclusion
404
References
405
17
SEMANTICS-BASED RESOURCE DISCOVERY IN
LARGE-SCALE GRIDS
409
Juan Li,
S
ame
e U.
Khan,
and Nas
i r G
hani
17.1
Introduction
410
17.2
Related Work
411
17.3
Virtual Organization Formation
412
17.3.1
Overview
412
17.3.2
Ontological Directories
412
XX
CONTENTS
17.3.3
Ontology Directory Lookup and VO Register
415
17.3.3.1
Exact Lookups
415
17.3.3.2
Browser-Bused Lookups
415
17.3.3.3
Keyword-Based Lookups
416
17.3.3.4
VO Rciiislcr
416
17.3.3.5
Directory Overlay Maintenance
417
17.4
Semantics-Based Resource Discovery in Virtual Organizations
417
17.4.1
Semantic Similarity
41
S
17.4.2
Illustrative Example
419
17.4.3
Semantics Based Topology Adaptation and Search
421
17.5
Prototype Implementation
und
revaluation
421
17.5.1
Implementation
421
17.5.2
GONID Toolkit Deployment and Evaluation
424
17.5.3
Evaluation Based on Simulation
425
17.6
Conclusion
427
References
427
18
GAME-BASED MODELS OF GRID USER S DECISIONS IN
SECURITY-AWARE SCHEDULING
431
Joanna
Kołodziej,
Samee U. Khan, Lizhe Wang, and Dan Chen
1
8.
1 Introduction
432
1
8.2
Security-Aware Scheduling Problems in Computational Grids
433
I
8.2.
1 Generic Model of Secure Grid Clusters
434
1
8.2.2
Security Criterion in Grid Scheduling
437
18.2.2.1
Risky Mode
440
1 8.2.3
Requirements of Grid End Users for Scheduling
440
18.3
Game Models in Security-Aware Grid Scheduling
441
1
8.3.1
Symmetric and Asymmetric Non-cooperative Games
of the End Users
442
18.3.1.1
Non-cooperative Symmetric Game
443
18.3.1.2
Asymmetric Scenario
—
Stackelberg Game
444
18.3.2
Cooperative and Semi-cooperative Game Scenarios
446
18.3.3
Online Scheduling Games
446
18.4
Case Study: Approximating the Equilibrium States of the End
Users Symmetric Game Using the Genetic Metaheuristics
447
18.4.1
Specification of the Game
448
18.4.
L
1
Characteristics of Game Players and Decision
Variables
448
CONTENTS XXI
18.4.1.2
Solving the Grid Users Game
448
18.4.1.3
Game Cost Functions
449
18.4.1.4
Task Execution Cost
450
18.4.1.5
Resource Utilization Cost
450
18.4.1.6
Security-Assurance Cost
451
18.4.2
Genetic-Based Resolution Methods for
Game Models
452
18.4.2.1
Schedulers Implemented in Global
Module
452
18.4.2.2
Local Schedulers in Players Module
453
18.4.3
Empirical Setup
454
1 8.4.3.1
Performance Measures
456
18.4.4
Results
457
18.5
Conclusion
460
References
460
19
ADDRESSING OPEN ISSUES ON PERFORMANCE
EVALUATION IN CLOUD COMPUTING
463
Beniamino Di Martino,
Massimo
Ficco,
Massimiliano
Rak,
and
Salvatore Venticinque
19.1
Introduction
464
19.2
Benchmarking Approaches
465
19.2.1
HPC-Like Benchmarking
465
19.2.2
Benchmark Standards
466
19.2.3
Cloud-Oriented Benchmarks
466
19.2.4
Benchmark as a Service Approach
467
19.2.5
Considerations
468
19.3
Monitoring in Cloud Computing
468
19.3.1
What Should Be Monitored
468
19.3.1.1
Infrastructure as a Service (IaaS)
469
19.3.1.2
Platform as a Service (PaaS)
470
19.3.1.3
Service as a Service (SaaS)
470
19.3.2
How to Monitor
472
19.3.2.1
Supporting Tools
473
19.4
Attack Countermeasures in Cloud Computing
474
19.5
Conclusion
480
References
480
xxii
CONTENTS
20
BROKER-MEDIATED CLOUD-AGGREGATION MECHANISM
USING MARKOVIAN QUEUES FOR SCHEDULING
BAG-OF-TASKS
(BOT)
APPLICATIONS
485
(ui/nw/i Xct fukanUi /vet ntnl lihiinulwuj
Vctravalli
20.1
Introduction 4N<>
20.2
Literal
niv
Re iew ami Contributions
48
20.2.1 1
.iteratila-
Review 4N
20.2.2
Contributions ami Scope of This Chapter 48s-
20.3
Problem
Sellini:
and Notations
20.4
Proposed
(
Ίοικί
Aggregation Mechanism
20.4.
1 I ask Distribution
lo Minimi/e
the
Application
Completion
Jíme
20.4.1.1
Integet
Approximation Techniques
20.4.
1
.2
Eliminating CSRs with Lower Resource
Capabilities
492
20.4.2
Task Distribution Based on Budget Requirements
493
20.5
Performance Evaluation and Discussions
494
20.5.
Analysis of Task Execution Time vs. Budget
Requirements
495
20.5.2
Analysis of the Total User Expenditure vs. Budget
Requirements
496
20.5.3
Analysis of Task Distribution Based on Budget
Requirements
496
20.6
Discussions
497
20.6.1
Applicability of Our Model to Divisible Load
Applications
497
20.6.2
Flexibility in Considering More User Requirements
497
20.6.3
Consideration of Cloud Characteristics
498
20.7
Conclusion
498
References
499
21
ON THE DESIGN OF A BUDGET-CONSCIOUS ADAPTIVE
SCHEDULER FOR HANDLING LARGE-SCALE MANY-TASK
WORKFLOW APPLICATIONS IN CLOUDS
503
Bharadwaj Veeravalli, Lingfang
Zeng,
and Xiaorong Li
21.1
Introduction
504
21.2
Related Work and Motivation
505
21.3
System Model and Problem Setting
506
21.3.1
System Model
506
CONTENTS XXIII
21.3.2
Many-Task Workflow Scheduling Problem
509
21.4
Proposed Scheduling Algorithm
5 12
21.4.1
Static ScaleStar
512
21.4.1.1
Initial Assignment Phase
513
21.4.1.2
Task Reassignment Phase
513
21.4.1.3
DeSiack Policy
513
21.4.2
Dynamic Adaptive Strategy
514
21.5
Performance Evaluation and Results
516
21.5.1
Evaluation Methodology
516
21.5.2
Real-World MTW Applications
517
21.5.3
Synthetic MTWs
519
21.5.4
Static Strategy Evaluation
519
21.5.5
Dynamic Strategy Evaluation
521
21.6
Conclusion
522
References
523
22
VIRTUALIZED ENVIRONMENT ISSUES IN THE CONTEXT
OF A SCIENTIFIC PRIVATE CLOUD
527
Bruno
Schulze,
Henrique
de Medeiros Klôh,
Matheus
Bousquet
Bandini,
Antonio Roberto
Mury,
Daniel Massami Muniz Yokoyama,
Victor Dias de
Oliveira, Fábio André Machado Porto, and
Giacomo
Victor
McEvoy Valenzano
22.1
Introduction
528
22.2
Related
Works
528
22.3
Methodology
531
22.3.1
Experiments and
Objectives
531
22.3.2
Experimental Infrastructure
531
22.4
Experiments
533
22.4.1
Experiment
1 :
Influence of the Hypervisors
on Performance
533
22.4.2
Experiment
2:
Hybrid
Virtualized
Environment
Evaluation
535
22.4.3
Experiment
3:
Virtualized Database
539
22.4.4
Result Analysis
543
22.5
Conclusion
544
22.6
Glossary
546
Acknowledgments
547
References
547
XXIV
CONTENTS
PART
5
OTHER TOPICS RELATED TO NETWORK-CENTRIC
COMPUTING AND ITS APPLICATIONS
23
IN-ADVANCE BANDWIDTH SCHEDULING IN
e-SCIENCE NETWORKS
551
Yan Li, Eunsung Jung, Sanjay
Ranka,
Nageswara S. Rao,
and
Surta
j
Salmi
23.
1 Introduction
552
23.2
Temporal Network Model
554
23.2.
1 Slotted Time
554
23.2.2
Continuous Time
555
23.3
Single-Path Scheduling
556
23.3.
1 Problem Definitions
556
23.3.2
Path Computation Algorithms
557
23.3.2.
1 Fixed Slot
557
23.3.2.2
Maximum Bandwidth in a Slot
560
23.3.2.3
Maximum Duration
560
23.3.2.4
First Slot
561
23.3.2.5
All Slots
562
23.3.2.6
All Pairs, All Slots
562
23.3.3
Performance Metrics
562
23.3.3.1
Space Complexity
563
23.3.3.2
Time Complexity
563
23.3.3.3
Effectiveness
564
23.3.4
Experiments
566
23.4
Multiple-Path Scheduling
570
23.4.1
Problem Definition
570
23.4.1.1
Data Structures
571
23.4.2
Optimal Solution and
N
-Batch Heuristics
572
23.4.2.1
N-Batch Heuristics
574
23.4.3
Online Scheduling Algorithms
575
23.4.3.1
Greedy Algorithm
575
23.4.3.2
Greedy Scheduling with Finish Time
Extension (GOS-E)
576
23.4.3.3
K-Path Algorithms
578
23.4.4
Experimental Evaluation
578
23.4.4.1
Experimental Framework
578
23.4.4.2
Single Start Time Scheduling (SSTS)
579
CONTENTS XXV
23.4.4.3 Multiple Start
Time Scheduling
(M STS)
582
23.4.4.4 GOSvs.GOS-E 585
23.5
Conclusion
587
Acknowledgment
587
References
587
24
ROUTING AND WAVELENGTH ASSIGNMENT IN OPTICAL
NETWORKS
591
Yan Li, Sanjay
Ranka,
and
Sa rtaj Sahni
24.
1 Introduction
592
24.2
Scheduling in
Ful
I-Wavelength Conversion Network
593
24.2.1
Problem Definition
593
24.2.2
Routing Algorithms
594
24.2.2.1
Modified Switch Path First
Algorithm (MSPF)
595
24.2.2.2
Modified Switch Window First
Algorithm (MSWF)
595
24.2.3
Wavelength Assignment Algorithms
596
24.2.4
Performance Evaluation
598
24.2.5
Experiments
599
24.2.5.1
Simulation Environment
599
24.2.5.2
Evaluated Algorithms
599
24.2.5.3
Results and Observations
600
24.2.6
Conclusions
602
24.3
Scheduling in Sparse Wavelength Conversion Network
603
24.3.1
Problem Description
603
24.3.2
Extended Network Model
604
24.3.3
Routing and Wavelength Assignment Algorithms
605
24.3.3.1
Extended Bellman-Ford Algorithm for
Sparse Wavelength Conversion
605
24.3.3.2
¿-Alternative Path Algorithm
606
24.3.3.3
Breaking the Ties in Path Selection
606
24.3.3.4
Wavelength Assignment
607
24.3.4
Experimental Evaluation
608
24.3.4.1
Experimental Framework
608
24.3.4.2
Slack Tie-Breaking Scheme
609
24.3.4.3
Blocking Probability
611
24.3.4.4
Requests Average Start Time
613
XXVi
CONTENTS
24.3.4.5
Scheduling Overhead
614
24.3.4.6
Algorithm Switching Strategy
615
24.3.5
Conclusions
616
Acknowledgment
617
References
617
25
COMPUTATIONAL GRAPH ANALYTICS FOR MASSIVE
STREAMING DATA
619
David ¡ùli
lie
к
Jason Riech;
David A, Bailer,
and Heiming Meyerhenke
25.
1 Introduction
620
25.2
STINGER: A General-Purpose Data Structure for
Dynamic Graphs
622
25.2.
1 Related Graph Data Structures
622
25.2.2
The STINGER Data Structure
622
25.2.3
Finding Parallelism in Streams and Analytics
624
25.3
Algorithm for Updating Clustering Coefficients
625
25.3.
1 Generic Algorithm
625
25.3.2
Approximating Clustering Coefficients Using a
Bloom Filter
626
25.4
Tracking Connected Components in Scale-Free Graphs
628
25.4.1
Problem Structure
628
25.4.2
The Algorithm in Detail
629
25.4.3
Discussion
631
25.5
Implementation
632
25.5.1
Multithreaded Platforms
632
25.5.2
The STINGER Data Structure
632
25.5.3
Multithreaded Implementation of Algorithm
25.1
(Clustering Coefficients)
633
25.5.4
Multithreaded Implementation of Algorithm
25.2
(Connected Components)
634
25.6
Experimental Results
634
25.6.1
Clustering Coefficient Experiments
634
25.6.1.1
Scalability of the Initial Computation
635
25.6.1.2
Number of Individual Updates per Second
635
25.6.2
Connected Components
637
25.7
Related Work
643
25.7.
J
Streaming Data
643
CONTENTS
XXVII
25.7.2
Graph Data Structures
643
25.7.3
Tracking Connected Components
643
25.8
Conclusion
644
Acknowledgments
645
References
645
26
KNOWLEDGE MANAGEMENT FOR FAULT-TOLERANT
WATER DISTRIBUTION
649
Jing Lin,
AU Huvson,
and
Salira
Sedigli
26.1
Introduction
650
26.2
Related Work
652
26.3
Agent-Based Model for WDN Operation
653
26.4
Classes in WDN Ontology Framework
656
26.4.1
WDN Ontology Class
656
26.4.2
Automatic Reasoning Based on Classes
658
26.5
Automated Failure Classification and Mitigation
659
26.5.1
Object Properties for Behavior Reasoning
659
26.5.2
Data Properties for Value Reasoning
666
26.6
Validation of Automated Failure Mitigation
668
26.6.1
Initial Configuration and Normal Operation
668
26.6.2
Failure Scenario and Automated Mitigation
670
26.7
Conclusion
674
Acknowledgment
675
References
675
Index
679
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| indexdate | 2024-07-10T00:56:04Z |
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| language | English |
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| spelling | Large scale network-centric distributed systems ed. by Hamid Sarbazi-Azad ... Hoboken, NJ Wiley 2014 LVIII, 700 S. Ill., graf. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series on parallel and distributed computing Netzwerk (DE-588)4171529-9 gnd rswk-swf Verteiltes System (DE-588)4238872-7 gnd rswk-swf Großtechnische Anlage (DE-588)4158306-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Verteiltes System (DE-588)4238872-7 s Großtechnische Anlage (DE-588)4158306-1 s Netzwerk (DE-588)4171529-9 s DE-604 Sarbazi-Azad, Hamid Sonstige oth Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026855145&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Large scale network-centric distributed systems Netzwerk (DE-588)4171529-9 gnd Verteiltes System (DE-588)4238872-7 gnd Großtechnische Anlage (DE-588)4158306-1 gnd |
| subject_GND | (DE-588)4171529-9 (DE-588)4238872-7 (DE-588)4158306-1 (DE-588)4143413-4 |
| title | Large scale network-centric distributed systems |
| title_auth | Large scale network-centric distributed systems |
| title_exact_search | Large scale network-centric distributed systems |
| title_full | Large scale network-centric distributed systems ed. by Hamid Sarbazi-Azad ... |
| title_fullStr | Large scale network-centric distributed systems ed. by Hamid Sarbazi-Azad ... |
| title_full_unstemmed | Large scale network-centric distributed systems ed. by Hamid Sarbazi-Azad ... |
| title_short | Large scale network-centric distributed systems |
| title_sort | large scale network centric distributed systems |
| topic | Netzwerk (DE-588)4171529-9 gnd Verteiltes System (DE-588)4238872-7 gnd Großtechnische Anlage (DE-588)4158306-1 gnd |
| topic_facet | Netzwerk Verteiltes System Großtechnische Anlage Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026855145&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT sarbaziazadhamid largescalenetworkcentricdistributedsystems |