Distributed computing: a locality-sensitive approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Philadelphia, Pa.
Society for Industrial and Applied Mathematics
2000
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| Schriftenreihe: | SIAM monographs on discrete mathematics and applications
5 ; 5 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 343 Seiten Illustrationen, Diagramme |
| ISBN: | 0898714648 |
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Datensatz im Suchindex
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Contents xv Preface 1 Introduction 1 1.1 What is distributed computing?. 1 1.2 A basic framework for distributed systems. 2 1.3 Issues unique to distributed computing. 4 1.3.1 Communication. 4 1.3.2 Incomplete knowledge. 4 1.3.3 Coping with failures. 5 1.3.4 Timing and synchrony. 5 1.3.5 Algorithmic and programming difficulties. 7 1.4 Locality-sensitive distributed algorithms. 8 1.4.1 Locality-preserving network representations. 8 1.4.2 Utilizing LP-representations for locality-sensitive computing. 10 P art I Basics of distributed network algorithms 13 2 Thedistributed network model 15 2.1 The model. 15 2.1.1 The system components. 15 2.1.2 Some graph
terminology. 16 2.1.3 The computational model. 19 2.1.4 Predefined structures. 20 2.2 Complexity measures. 21 2.2.1 Time complexity. 21 2.2.2 Space complexity. 22 2.2.3 Message complexity . 23 2.2.4 Weighted cost measures for communication tasks. 24 2.2.5 Global and specific complexity bounds. 25 2.3 Three representative models. 26 3 Broadcast and convergecast 31 3.1 The broadcast operation. 31 3.2 Tree broadcast. 31 3.3 The flooding algorithm. 33 3.4
Convergecasts. 34 3.4.1 Acknowledgement echos. 34
Contents viii 3.4.2 3.4.3 Global function computation. 36 Pipelined broadcasts and convergecasts . 38 4 Downcasts and upcasts 41 4.1 Downcasts. 41 4.2 Upcasts. 42 4.2.1 Ranked items. 42 4.2.2 Ordered items . 43 4.2.3 Unordered items. 43 4.3 Applications. 44 4.3.1 Smallest k-oi-m. 44 4.3.2 Information gathering and dissemination. 45 4.3.3 Route-disjoint matching.45 4.3.4 Token distribution. 46 5 Tree constructions 49 5.1 BPS tree
construction. 49 5.2 Layer-synchronized BFS construction. 50 5.3 Update-based BFS construction. 52 5.4 Distributed DFS. 54 5.5 MST. 55 5.5.1 A distributed variant of Prim’s algorithm. 56 5.5.2 The synchronous GHS algorithm. 57 5.6 Faster upcasting on matroid problems. 60 5.6.1 Matroid problems . 60 5.6.2 A distributed algorithm for matroids. 62 5.6.3 Analysis. 63 5.6.4 An alternative MST construction algorithm. 65 6 Synchronizers 69 6.1 The synchronizer methodology. 69 6.1.1 Underlying simulation principles.
69 6.1.2 Conceptual implementation strategy. 71 6.1.3 Implementation phases.72 6.1.4 Alternative conceptual implementation. 72 6.2 Complexity measures for synchronizers.73 6.3 Two basic synchronizers. 74 6.3.1 Synchronizer a. 74 6.3.2 Synchronizer ß. 75 7 Vertex coloring 79 7.1 The coloring problem. 79 7.2 A basic palette reduction procedure . 80 7.3 3-coloring trees and bounded-degree graphs. 81 7.4 (Δ + l)-coloring for arbitrary graphs. 84 7.5 A lower bound for 3-coloring the ring. 85
ix Contents 8 Maximal independent sets (MIS) 91 8.1 Basic distributed MIS algorithms. 91 8.1.1 Sequential greedy MIS construction . 91 8.1.2 Distributed implementation. 92 8.2 Reducing coloring to MIS. 93 8.3 Constructing small dominating sets on trees. 95 8.4 Randomized distributed MIS algorithm . 97 8.5 PRAM-based deterministic MIS algorithm. 101 9 Message routing 103 9.1 The routing problem. 103 9.2 Shortest paths routing. 105 9.3 Compact routing. 106 9.3.1 Quality measures.107 9.3.2 Two basic solutions . 108 9.3.3 Name-based routing schemes. 109 9.4 Deadlock-free routing
. 109 9.4.1 The buffer-class approach. 110 9.4.2 Individual buffer-routing. Ill 9.4.3 Increasing class buffer-routing.Ill 10 Local queries and local resource finding 113 10.1 Local queries. 113 10.1.1 Single-shot local queries. 113 10.1.2 Repeated local queries. 114 10.2 Local resource finding. 115 10.2.1 The problem. 115 10.2.2 Formal statement and relevant parameters. 116 10.2.3 Exact solution . 117 10.2.4 Three policies revisited . 118 10.2.5 The global matching paradigm. 118 Part II Locality-preserving representations 121
11 Clustered representations: Clusters, covers and partitions 123 11.1 The graph model. 123 11.2 Clusters, covers and partitions . 123 11.2.1 Clusters. 123 11.2.2 Covers and partitions . 124 11.2.3 Evaluation criteria. 125 11.3 Locality measures and neighborhoods . 125 11.3.1 Cluster radius and diameter. 125 11.3.2 Neighborhoods. 126 11.4 Sparsity measures. 126 11.4.1 Cover sparsity . 126 11.4.2 Partition sparsity. 127 11.5 Example: A basic construction. 129 11.6 Some additional
variants. 131 11.6.1 Separated partial partitions. 131
X Contents 11.6.2 Coarsening. 131 11.6.3 Weak clusters. 132 12 Sparse covers 135 12.1 Basic goals . 135 12.2 Low average-degree covers.135 12.2.1 Algorithm Av_Cover. 135 12.2.2 Analysis. 136 12.3 Partial partitions. 138 12.3.1 Procedure Part. 138 12.3.2 Analysis. 140 12.3.3 Constructing s-separated partial partitions. 140 12.4 Low maximum-degree covers . 141 12.4.1 Algorithm Max.Cover. 141 12.4.2
Analysis. 142 12.4.3 Covers based on s-separated partial partitions. 143 13 Sparse partitions 147 13.1 Low average-degree partitions. 147 13.1.1 The average cluster-degree partition algorithmAv_PARTc. 147 13.1.2 The average vertex-degree partition algorithmAv_Part„. 147 13.2 Low maximum-degree partitions . 149 13.2.1 The maximum vertex-degree partition algorithmMax_Part„. 149 13.2.2 The maximum cluster-degree partition algorithmMax_Partc. 151 13.3 Other types of partitions. 153 14 Related graph representations 155 14.1 Hierarchical cover constructions. 155 14.1.1 The refinement hierarchy. 155 14.1.2 Constructing a refinement hierarchy. 156 14.1.3 Properties. 156 14.2 Network decomposition . 157 14.2.1 Decomposition
algorithmDecomp. 158 14.2.2 Properties. 159 14.3 Regional matchings. 159 14.3.1 Regional matching construction. 160 14.3.2 Properties. 161 14.4 Cardinality-based clusters. 161 15 Skeletal representations: Spanning trees, tree covers and spanners 165 15.1 Representation types and relevant parameters. 165 15.1.1 Locality and sparsity. 165 15.1.2 Size and girth. 166 15.2 Global skeletal representations: Spanning trees. 166 15.2.1 Basic types of spanning trees. 167 15.2.2 Controlling tree degrees. 167 15.3 Minimum total distance trees. 169 15.4
Proximity-preserving spanners. 170 15.5 Tree covers. 171 15.5.1 Tree cover construction . 172
Contents xi 15.5.2 Properties. 173 16 Sparse spanners for unweighted graphs 177 16.1 Sparse spanners for arbitrary graphs. 177 16.1.1 Constructing a spanner for unweighted graphs . 177 16.1.2 Analysis. 177 16.2 Lower bounds. 179 16.3 Examples.181 16.3.1 Spanners for the hypercube. 181 16.3.2 Spanners for chordal graphs. 183 16.4 Directed variants. 187 17 Light-weight spanners 191 17.1 Light, low-stretch trees. 191 17.1.1 MST, SPT and SLT. 191 17.1.2 The SLT algorithm. 192 17.1.3
Analysis. 194 17.2 Light, sparse, low-stretch spanners. 196 17.2.1 A tree-cover based algorithm. 196 17.2.2 A greedy sequential algorithm. 197 17.2.3 Algorithm Light_Span.199 18 Spanners with low average stretch 207 18.1 Basic properties of average stretch spanners. 207 18.2 Low average stretch trees. 208 18.2.1 Examples. 209 18.2.2 A lower bound on average stretch trees . 209 18.3 Constructing average stretch trees on unweighted graphs.211 18.3.1 Restricting attention to sparse multigraphs. 211 18.3.2 Overview of the construction. 212 18.3.3 The construction algorithm. 214 18.3.4
Analysis. 214 18.4 Constructing average stretch trees on weighted graphs.215 18.4.1 Overview. 215 18.4.2 The construction algorithm. 216 18.4.3 Analysis. 217 18.5 Light trees with low average stretch . 218 19 Proximity-preserving labeling systems 221 19.1 Adjacency-preserving labelings. 221 19.1.1 Adjacency labeling for trees. 222 19.1.2 Graphs with bounded arboricity.223 19.1.3 Universal graphs. 223 19.2 Distance-preserving labelings. 224 19.3 Distance-approximating labelings. 227 19.4 Interval-tree labeling. 228
xii Part III Contents Distributed constructions and applications of LP-representations 231 20 A basic algorithm for constructing network partitions 233 20.1 Overall structure of the algorithm .233 20.2 The cluster construction procedure Cluster_Cons . 234 20.3 The next center selection procedure Next_Ctr. 234 20.4 The intercluster edge selection procedure Rep_Edge. 235 20.5 Analysis. 236 20.6 Improvements. 237 21 Efficient algorithms for constructing covers 239 21.1 Fast synchronous distributed algorithms.239 21.2 Low communication synchronous algorithms. 243 21.2.1 Basic procedures.244 21.2.2 Average-degree coarsening. 249 21.2.3 Maximum-degree coarsening. 249 21.2.4 Synchronous distributed implementations. 250 22 Efficient algorithms for constructing network decompositions 255 22.1
Constructing s-separated, r-ruling sets. 255 22.2 Decomposition construction. 257 23 Exploiting topological knowledge: Broadcast revisited 261 23.1 Restricted flooding using preconstructed structures. 261 23.1.1 Quality measure. 261 23.1.2 Flooding on a subgraph. 262 23.1.3 Selecting an appropriatesubgraph. 262 23.2 Topological knowledge and broadcast. 263 23.2.1 Three models of topological knowledge. 264 23.2.2 Topological knowledge and message complexity of broadcast. 264 23.3 The message complexity of broadcast in ΚΊ\. 265 23.3.1 The traveler algorithm. 265 23.3.2 The lower bound proof. 266 23.4 Partial topological knowledge. 269 23.4.1 A hierarchy of topological knowledgemodels. 269 23.4.2 The information-communication trade-
off. 270 23.4.3 The к — Flood algorithm. 271 24 How local are global tasks? MST revisited 273 24.1 Existential versus universal optimality.273 24.2 A fast MST algorithm. 275 24.2.1 Overview of the algorithm. 275 24.2.2 Stage I: Controlled-GHS . 277 24.2.3 Stage II: Edge elimination. 279 24.2.4 The complexity of the combined algorithm . 279 24.3 A lower bound. 280 24.3.1 A lower bound for the mailing problem on Fm.280 24.3.2 A lower bound for the MST problem on j?m. 283
Contents xiii 25 Local coordination: Synchronizers and MIS revisited 289 25.1 Locality-based synchronizers.289 25.1.1 Synchronizer 7. 289 25.1.2 Synchronizer 5. 291 25.1.3 A lower bound for spanner-basedsynchronizers. 292 25.2 Deterministic decomposition-basedMIS algorithm. 292 26 Hierarchical cluster-based routing 295 26.1 Interval tree routing. 295 26.1.1 The scheme. 295 26.1.2 Analysis.296 26.1.3 ITR for general networks.296 26.2 The routing scheme . 297 26.2.1 Partial routing schemes.297 26.2.2 Overview of the hierarchical scheme. 298 26.2.3 Regional routing
schemes.298 26.2.4 The hierarchical routing scheme . 300 26.3 Low dilation, low buffer deadlock-free routing. 301 26.3.1 ITR buffer-routing . 301 26.3.2 Tree-cover routing. 302 27 Regional directories: Resource finding revisited 305 27.1 Answering local queries using regional directories. 305 27.2 Local resource finding using regional directories. 306 27.2.1 The hierarchical directory controller.306 27.2.2 Achieving a 1-dilated resource controller. 308 27.3 A lower bound for regional resource finding.309 27.3.1 A yardstick for updates. 309 27.3.2 The lower bound. 310 27.4 A controller based on a refinement hierarchy. 312 27.4.1 Control
algorithms. 312 27.4.2 Correctness and analysis. 313 27.4.3 Modification: Exact counting. 314 28 Additional applications in other settings 317 Bibliography 319 Index 339 |
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| spelling | Peleg, David Verfasser aut Distributed computing a locality-sensitive approach David Peleg Philadelphia, Pa. Society for Industrial and Applied Mathematics 2000 XVI, 343 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier SIAM monographs on discrete mathematics and applications 5 5 Traitement réparti Datenverarbeitung Electronic data processing Distributed processing Datenverarbeitungssystem (DE-588)4125229-9 gnd rswk-swf Verteiltes Datenverarbeitungssystem rswk-swf Dezentrale Datenverarbeitung (DE-588)7545389-7 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Verteiltes System (DE-588)4238872-7 gnd rswk-swf Verteiltes System (DE-588)4238872-7 s Dezentrale Datenverarbeitung (DE-588)7545389-7 s Datenverarbeitungssystem (DE-588)4125229-9 s DE-604 Datenverarbeitung (DE-588)4011152-0 s 1\p DE-604 Verteiltes Datenverarbeitungssystem s SIAM monographs on discrete mathematics and applications 5 ; 5 (DE-604)BV025247686 5 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010286863&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Peleg, David Distributed computing a locality-sensitive approach SIAM monographs on discrete mathematics and applications Traitement réparti Datenverarbeitung Electronic data processing Distributed processing Datenverarbeitungssystem (DE-588)4125229-9 gnd Verteiltes Datenverarbeitungssystem Dezentrale Datenverarbeitung (DE-588)7545389-7 gnd Datenverarbeitung (DE-588)4011152-0 gnd Verteiltes System (DE-588)4238872-7 gnd |
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| title | Distributed computing a locality-sensitive approach |
| title_auth | Distributed computing a locality-sensitive approach |
| title_exact_search | Distributed computing a locality-sensitive approach |
| title_full | Distributed computing a locality-sensitive approach David Peleg |
| title_fullStr | Distributed computing a locality-sensitive approach David Peleg |
| title_full_unstemmed | Distributed computing a locality-sensitive approach David Peleg |
| title_short | Distributed computing |
| title_sort | distributed computing a locality sensitive approach |
| title_sub | a locality-sensitive approach |
| topic | Traitement réparti Datenverarbeitung Electronic data processing Distributed processing Datenverarbeitungssystem (DE-588)4125229-9 gnd Verteiltes Datenverarbeitungssystem Dezentrale Datenverarbeitung (DE-588)7545389-7 gnd Datenverarbeitung (DE-588)4011152-0 gnd Verteiltes System (DE-588)4238872-7 gnd |
| topic_facet | Traitement réparti Datenverarbeitung Electronic data processing Distributed processing Datenverarbeitungssystem Verteiltes Datenverarbeitungssystem Dezentrale Datenverarbeitung Verteiltes System |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010286863&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV025247686 |
| work_keys_str_mv | AT pelegdavid distributedcomputingalocalitysensitiveapproach |