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課
程
大
綱
摘
要
表
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次序
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課 程 主 題
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教 學 內 容
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1
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Introduction to Cloud computing
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l Cluster computing
l Grid computing
l Web services
l Cloud computing
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2
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The Anatomy of Cloud computing
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l Infrastructure
l Middleware
l Software
l Services
l Applications
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3
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File System Overview
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l Files
l Directories
l Disks
l Issues
l Example Systems
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4
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Distributed File Systems: Issues
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l Naming
l Authentication and Access Control
l Batched Operations
l Caching
l Concurrency Control
l Locking implementation
l Replication
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5
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Distributed File Systems: Implementation
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l Network File System (NFS)
l Andrew File System (AFS)
l Storage Resource Broker (SRB)
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6
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Distributed File Systems and Cloud Computing
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l Google File System (GFS)
l Hadoop HDFS
l Discussion
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7
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Workload model
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l Job model
l Resource model
l Performance metrics
l Evaluation methods
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8
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Job scheduling
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l SPMD job scheduling
l DAG scheduling
l Mixed-parallel scheduling
l Online multiple workflow scheduling
l Backfilling scheduling
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9
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Resource allocation
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l Resource fragmentation
l Co-allocation
l Moldability issues
l Heterogeneous resource allocation
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10
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Hadoop Introduction
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l Hadoop Histroy
l Hadoop Architecture
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11
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Hadoop key component (I)
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l Hadoop Distributed File System
l Hadoop Installation: presudo mode
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12
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Hadoop key component (II)
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l Map Reduce Programming
l Example: Wordcount
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13
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Hadoop Cluster
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l Hadoop Installation: cluster mode
l Hadoop Deployment Tool : SmartFog and DRBL
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14
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Hadoop Permission
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l HDFS multi-user permission setup
l Hadoop Testbed
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15
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Overview for image/video retrieval and analysis
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l Applications for large-scale image/video retrieval and analysis
l Image/video representations
l Challenges in large-scale image/video collections
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16
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Image similarity and graph construction
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l Image similarities
l (Large-scale) Image graph construction
l Applications for image graph (e.g., canonical image selection, query expansion)
l Implementation by MapReduce
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17
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Image clustering
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l Clustering algorithms (K-means, agglomerative, pLSA) for image collections
l Implementation by MapReduce
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18
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Image/video Analysis
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l Semantic concepts (e.g., large-scale concept ontology)
l Machine learning for concept detection
l Implementation by MapReduce
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參考
書目
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1. Operating Systems: Design and Implementation, Andrew S. Tanenbaum, Prentice-Hall, 2006
2. Task Scheduling for Parallel Systems, Oliver Sinnen, Wiley Inter-Science, 2007
3. Pro Hadoop, Jason Venner, Apress, 2009, DOI: 10.1007/978-1-4302-1943-9,
ISBN: 978-1-4302-1942-2 (Print) 978-1-4302-1943-9 (Online),
4. Designing and Building Parallel Programs, Ian Foster, Addison Wesley, 1995
5. Cloud Computing: A Practical Approach, Velte, Anthony T./ Velte, Toby J./ Elsenpeter, Robert , McGraw Hill 2009Designing and Building Parallel Programs, Ian Foster, Addison Wesley, 1995
6. Parallel Programming in C with MPI and OpenMP, Michael J. Quinn, McGraw Hill 2003
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