Suchen und Finden
Preface
5
Contents
8
Part I Energy Efficiency
13
Energy-Efficient and High-Performance Processing of Large-Scale Parallel Applications in Data Centers
14
1 Introduction
14
1.1 Motivation
14
1.2 Our Contributions
16
2 Related Work
17
3 Preliminaries
18
3.1 Power and Task Models
19
3.2 Problems
21
3.3 Lower Bounds
21
4 Heuristic Algorithms
22
4.1 Precedence Constraining
22
4.2 System Partitioning
23
4.3 Task Scheduling
25
5 Optimal Energy/Time/Power Allocation
26
5.1 Minimizing Schedule Length
26
5.1.1 Level 1
26
5.1.2 Level 2
27
5.1.3 Level 3
27
5.1.4 Level 4
28
5.2 Minimizing Energy Consumption
32
5.2.1 Level 1
32
5.2.2 Level 2
32
5.2.3 Level 3
33
5.2.4 Level 4
33
6 Simulation Data
36
7 Summary and Future Research
43
References
44
Energy-Aware Algorithms for Task Graph Scheduling, Replica Placement and Checkpoint Strategies
47
1 Introduction
47
2 Energy Models
49
2.1 Literature Survey
50
2.1.1 DVFS and Optimization Problems
51
2.1.2 Energy Models
52
2.2 Example
52
3 Minimizing the Energy of a Schedule
54
3.1 Optimization Problem
54
3.2 The CONTINUOUS Model
55
3.2.1 Special Execution Graphs
56
3.2.2 General DAGs
57
3.3 Discrete Models
57
3.3.1 The VDD-HOPPING Model
58
3.3.2 NP-Completeness and Approximation Results
58
3.4 Final Remarks
59
4 Replica Placement
59
4.1 Framework
60
4.1.1 Replica Servers
61
4.1.2 With Power Consumption
62
4.1.3 Objective Functions
63
4.1.4 Summary of Results
63
4.2 Complexity Results: Update Strategies
64
4.2.1 Running Example
64
4.2.2 Dynamic Programming Algorithm
65
4.3 Complexity Results with Power
67
4.3.1 Running Example
67
4.3.2 NP-Completeness of MINPOWER
68
4.3.3 A Pseudo-polynomial Algorithm for MINPOWER-BOUNDEDCOST
70
4.4 Simulations
71
4.4.1 Impact of Pre-existing Servers
71
4.4.2 With Power Consumption
73
4.4.3 Running Time of the Algorithms
74
4.5 Concluding Remarks
74
5 Checkpointing Strategies
75
5.1 Framework
76
5.1.1 Model
76
5.1.2 Optimization Problems
77
5.2 With a Single Chunk
78
5.2.1 SINGLESPEED Model
78
5.2.2 MULTIPLESPEEDS Model
79
5.3 Several Chunks
80
5.3.1 Single Speed Model
81
5.3.2 Multiple Speeds Model
82
5.4 Simulations
83
5.4.1 Simulation Settings
83
5.4.2 Comparison with Single Speed
85
5.4.3 Comparison Between EXPECTED-DEADLINE and Hard-Deadline
86
5.5 Concluding Remarks
86
6 Conclusion
87
References
88
Energy Efficiency in HPC Data Centers: Latest Advances to Build the Path to Exascale
91
1 Introduction
91
2 Computing Systems Architectures
92
2.1 Architecture of the Current HPC Facilities
92
2.2 Overview of the Main HPC Components
95
2.3 HPC Performance and Energy Efficiency Evaluation
99
3 Energy-Efficiency in HPC Data-Center: Overview & Challenges
102
3.1 The Exascale Challenge
102
3.2 Hardware Approaches Using Low-Power processors
103
3.3 Energy Efficiency of Virtualization Frameworks over HPC Workloads
105
3.4 Energy Efficiency in Resource and Job Management Systems (RJMSs)
110
4 Conclusion: Open Challenges
114
References
115
Techniques to Achieve Energy Proportionality in Data Centers: A Survey
118
1 Introduction
118
2 Energy Proportionality
120
2.1 Energy Proportionality at the Server Level
121
2.2 Energy Proportionality at Data Center Level
123
2.3 Overview on Power Proportionality Techniques at Different Data Center Levels
124
3 Energy Proportionality at Component Level
127
3.1 Energy Proportionality at the CPU
127
3.2 Energy Proportionality at the Memory
129
3.3 Energy Proportionality at the Disk
131
3.4 Energy Proportionality at the Networking Interface
132
4 Power Management Techniques at Server Level
133
5 Data Center/Cluster Level Power Management
135
5.1 Server Provisioning in Internet Data Centers (IDCs)
136
5.2 Virtual Machine Management
144
5.3 Other Data Center Level Power Management Techniques
148
6 Energy Cost Minimization Through Workload Distribution Across Data Centers
152
7 Data Center Simulation Tools
157
8 Performance of Server and Data Center Level Power Management Techniques
159
9 Conclusions
161
References
162
A Power-Aware Autonomic Approach for Performance Management of Scientific Applications in a Data Center Environment
172
1 Introduction
172
2 Background
175
3 An Online Look-Ahead Control-based Management Approach
182
4 Case Study: Performance Management of a Parallel Loop Execution Environment
187
5 Benefits of the Proposed Approach
193
6 Combining DLS Techniques with the Proposed Approach
194
7 Conclusion
195
References
196
CoolEmAll: Models and Tools for Planning and Operating Energy Efficient Data Centres
199
1 Introduction
199
1.1 The CoolEmAll Project
202
1.2 RelatedWork
204
2 Simulation, Visualisation and Decision Support Toolkit
205
2.1 Architecture
206
2.2 Application Profiler
208
2.3 Data Center Workload and Resource Management Simulator
209
2.3.1 Architecture
209
2.3.2 Workload Modelling
210
2.3.3 Resource Description
211
2.3.4 Simulation of Energy Efficiency
212
2.3.5 Application Performance Modelling
213
2.4 Interactive Computational Fluid Dynamics Simulation
214
2.5 Visualization
216
3 Data centre Efficiency Building Blocks
217
3.1 DEBB Concept and Structure
217
3.2 Hardware Models for Workload Simulation
220
3.2.1 Hardware Modelling in DCworms Workload Simulator
220
3.2.2 Hardware Power Profiles
222
3.2.3 Electrical Model of the Power Supply Unit 2.0
222
3.3 Hardware Models for Thermodynamic Profiles and Cooling Equipment
223
3.4 Hardware Models for CFD Simulation
225
3.5 Assessment of DEBBs
227
4 Energy Efficiency Metrics
227
4.1 State of the Art
228
4.2 Selected Metrics for CoolEmAll
229
4.2.1 Resource Usage Metrics
230
4.2.2 Energy Based Metrics
231
4.2.3 Heat-Aware Metrics
232
4.3 Application Power Model
233
5 Validation of the CoolEmAll Approach
234
5.1 Validation Approach
234
5.1.1 Capacity Management
236
5.1.2 Optimisation of Rack Arrangement in a Compute Room Using Open Data Centre Building Blocks
236
5.1.3 Analysis of Free Cooling Efficiency for Various Inlet Temperatures
237
5.2 Testbed
237
5.3 Analysis and Optimization of Data Centre Efficiency
239
5.3.1 Capacity Management
239
5.3.2 Analysing Cooling Efficiency in Compute-room
246
6 Business Impact
248
7 Summary
250
References
251
Smart Data Center
254
1 Introduction
254
2 System Model
255
2.1 Long Term Power Purchase
256
2.2 Real Time Power Purchase
257
3 Constraints
257
3.1 Purchasing Accuracy and Cost
257
3.2 Data Center Availability
258
3.3 UPS Lifetime
258
4 Cost Minimization
259
5 Algorithm Design
259
5.1 Drift Plus Penalty Upper Bound
260
5.2 Relaxed Optimization
262
5.3 Two Timescale Smart Data Center Algorithm
263
6 Performance Analysis
264
7 Related Work
267
8 Conclusions
267
References
268
Power and Thermal Efficient Numerical Processing
270
1 Introduction
270
2 Floating-Point Representation
271
2.1 Formats
272
2.2 Rounding Modes
272
2.3 Operations
273
2.4 Exceptions
273
3 Floating-Point Addition
273
4 Floating-Point Multiplication
275
5 Floating-Point Fused Multiply-Add
277
6 Floating-Point Division
279
6.1 Division by Digit Recurrence
279
6.1.1 Radix-4 Division Algorithm
280
6.1.2 Intel Penryn Division Unit
281
6.1.3 Radix-16 by Overlapping Two Radix-4 Stages
281
6.2 Division by Multiplication
283
7 Energy dissipation in FP-units
286
7.1 Energy Metrics
286
7.2 Implementation of the FP-Units
287
7.3 Energy Consumption in Floating-Point Workloads
288
7.4 Thermal Analysis
290
8 Conclusions and Outlook on FP-Units
292
References
292
Providing Green Services in HPC Data Centers: A Methodology Based on Energy Estimation
294
1 Introduction
294
2 Identifying Operations in a Service
297
2.1 Fault Tolerance Case
297
2.2 Data Broadcasting Case
298
2.3 Associated Parameters
299
3 Energy Calibration Methodology
300
3.1 Calibration of the Power Consumption op
301
3.2 Calibration of the Execution Time top
302
3.2.1 Fault Tolerance Case
303
3.2.2 Data Broadcasting Case
304
4 Energy Estimation Methodology
305
4.1 Fault Tolerance Case
306
4.1.1 Checkpointing
307
4.1.2 Message Logging
307
4.1.3 Coordination
308
4.2 Data Broadcasting Case
309
4.2.1 MPI/SAG and Hybrid/SAG
309
4.2.2 MPI/Pipeline and Hybrid/Pipeline
310
5 Validation of the Estimations
311
5.1 Calibration Results of the Platform
311
5.1.1 Calibrating the Power Consumption
311
5.1.2 Calibration of the Execution Time
314
5.2 Accuracy of the Estimations
320
5.2.1 Fault Tolerance Case
321
5.2.2 Data Broadcasting Case
323
6 Energy-Aware Choice of Services for HPC applications
325
6.1 Fault Tolerance Protocols
325
6.2 Data Broadcasting Algorithms
326
7 Conclusion
327
References
329
Part II Networking
331
Network Virtualization in Data Centers: A Data Plane Perspective
332
1 Introduction
332
1.1 Network Link Virtualization
333
1.2 Network Node Virtualization
333
1.3 Organization
334
2 Flexible Flow Matching for Network Link Virtualization
334
2.1 Background
334
2.2 Existing Solutions
336
2.3 Algorithmic Solution for Efficient Flexible Flow Matching
337
2.3.1 Motivations
337
2.3.2 Algorithms
339
2.3.3 Architecture
340
2.4 Performance Evaluation
342
2.4.1 Experimental Setup
342
2.4.2 Algorithm Evaluation
342
2.4.3 Hardware Implementation
344
3 Resource Consolidation in Network Node Virtualization
344
3.1 Background
345
3.2 Existing Solutions
346
3.3 Efficient Algorithm for Resource Consolidation
346
3.3.1 Motivations
346
3.3.2 Trie Merging
348
3.3.3 Lookup Process
349
3.3.4 Traffic Isolation
349
3.4 Analysis and Evaluation
350
3.4.1 Theoretical Comparison
350
3.4.2 Experimental Setup
350
3.4.3 Scalability
351
3.4.4 Execution Time
352
4 Summary and Discussion
352
References
353
Optical Data Center Networks: Architecture, Performance, and Energy Efficiency
355
1 Introduction
355
2 Optical Switches Used in Optical Data Center Networks
357
2.1 Optical Packet Switches
357
2.2 Optical Circuit Switches
358
3 Approach 1: Optical Data Center Networks to Provide Large Bandwidth for All-to-All Communication
360
3.1 Optical Packet Switches with Large Bandwidth
361
3.2 Data Center Network Structure Using Optical Packet Switches
362
3.2.1 Connection Within Group
364
3.2.2 Connection Between Groups
364
3.2.3 Routing in Topology
364
3.3 Parameter Settings
366
3.3.1 Parameters for Connection Between Groups
367
3.3.2 Parameters for Connection within Group
367
3.4 Evaluation
368
3.4.1 Topologies
368
3.4.2 Properties of Topologies
370
3.4.3 Maximum Link Load
373
4 Approach 2: Networks to Achieve Low Energy Consumption
374
4.1 Overview
376
4.2 Virtual Network Topologies Suitable for Optical Data Center Networks
377
4.2.1 Requirements
377
4.2.2 Existing Network Structures for Data Centers
378
4.2.3 Generalized Flattened Butterfly
380
4.3 Control of Virtual Network Topology to Achieve Low Energy Consumption
388
4.3.1 Outline
388
4.3.2 Control of Topology to Satisfy Requirements
389
4.4 Evaluation
391
5 Conclusion
393
References
394
Scalable Network Communication Using Unreliable RDMA
396
1 Introduction
396
1.1 The Significance of Data Communication
397
1.2 Datacenter Computing and RDMA
399
1.3 High-Performance Computing and RDMA
399
1.4 RDMA and the Current Unreliable Datagram Network Transports
400
2 Overview of RDMA Technology
401
2.1 Overview of the iWARP Standard
402
2.2 Overview of the InfiniBand Standard
404
3 The Case for RDMA over Unreliable Transports
405
3.1 Importance of Unreliable Connectionless RDMA
405
3.2 Benefits of RDMA over Unreliable Datagrams for iWARP
406
4 RDMA over Unreliable Datagrams
408
4.1 Related Work and Development History
409
4.2 iWARP Extension Methodology
410
4.3 iWARP Design Changes
410
4.4 RDMA Write-Record
413
4.5 Packet Loss Design Considerations
416
5 Datagram-iWARP Software Implementation
416
5.1 iWARP Socket Interface
418
6 Experimental Results and Analysis
418
6.1 Verbs-Layer Microbenchmarks
419
6.2 Send/Recv Broadcast
419
6.3 Packet Loss and Performance
420
6.4 Datacenter Application Results
422
7 Summary
425
References
426
Packet Classification on Multi-core Platforms
428
1 Introduction
428
2 Background
429
2.1 Multi-field Packet Classification
429
2.2 Related Work
430
2.3 Multi-core Processor
431
3 Decision-Tree Based Approaches
432
3.1 Algorithms
432
3.2 Challenges and Prior Work
434
4 Decomposition-Based Approaches
435
4.1 Overview
435
4.2 Challenges and Prior Work
436
4.3 Preprocessing
437
4.4 Searching
440
4.5 Merging
441
5 Performance Evaluation and Summary of Results
441
5.1 Experimental Setup
441
5.2 Latency
443
5.3 Throughput
444
5.4 Cache Performance
445
5.5 Impact of the Number of Threads
447
5.6 Comparison with Existing Approaches
447
6 Conclusion
449
References
449
Optical Interconnects for Data Center Networks
451
1 Introduction
451
2 Need for Optical Interconnects in Data Center Networks
452
3 Optical Components in Data Centers
455
3.1 Semiconductor Optical Amplifier (SOA)
456
3.2 Silicon Micro Ring Resonator
456
3.3 ArrayedWaveguide Grating
456
3.4 Wavelength Selective Switch
458
3.5 MEMS Switch(Optical Switching Matrix, Optical Crossbar)
459
3.6 Circulators
461
3.7 Optical Multiplexer and De-multiplexer
461
4 Optical Interconnects in Data Center Networks and their Performance
461
4.1 Reconfigurable Architectures
461
4.1.1 An Enhanced Optically Connected Network Architecture
462
4.1.2 OSA, a Novel Optical Switching Architecture for DCNs
462
4.1.3 Wavelength-reconfigurable optical packet and circuit switched platform for DCNs
463
4.1.4 Next-Generation Optically-Interconnected High-Performance Data Centers
464
4.1.5 The Data Vortex Optical Packet Switched Interconnection Network
465
4.1.6 Proteus: A Topology Malleable Data Center Network
465
4.1.7 A Hybrid Optical Packet and Wavelength Selective Switch for High-Performance DCNs
466
4.2 Power Saving Architectures
467
4.2.1 VCSEL Based Energy Efficient and Bandwidth Reconfigurable Architecture
467
4.2.2 A Wavelength Striped, Packet Switched, Optical Interconnection Network
468
4.2.3 SPRINT: Scalable Photonic Switching Fabric for HIGH PERFORMANCE COMPUTING
468
4.3 Low Latency Architectures
470
4.3.1 DOS: A Scalable Optical Switch for Data Centers
470
4.3.2 Scalable Optical Packet Switch Architecture for Low Latency and High Load
471
4.3.3 AWGR Based Data Center Switches Using RSOA-based Optical Mutual Exclusion
472
4.3.4 A Petabit Photonic Packet Switch (P3S)
472
4.3.5 Optical Interconnection Networks: The OSMOSIS Project
473
4.3.6 A Scalable Optical Multi-Plane Interconnection Architecture
474
4.3.7 Low Latency and Large Port Count OPS for Data Center Network Interconnects
474
4.4 Link Bandwidth Scaling Architectures
476
4.4.1 Data Center Network Based on Flexible Bandwidth MIMO OFDM Optical Interconnects
476
4.4.2 Photonic Terabit Routers Employing WDM
477
4.5 High Radix Switch Design
478
5 Data center traffic characteristics
478
6 Energy Requirements for Data Center Networks
480
7 Routing in Data Centers
482
References
483
TCP Congestion Control in Data Center Networks
486
1 Introduction
486
2 TCP Impairments in Data Center Networks
487
2.1 TCP Incast
488
2.2 TCP Outcast
489
2.3 Queue Buildup
490
2.4 Buffer Pressure
491
2.5 Pseudo-Congestion Effect
491
2.6 Summary: TCP Impairments and Causes
492
3 TCP Variants for Data Center Networks
493
TCP with FG-RTO + Delayed ACKs Disabled [3]
493
3.3.1 Explicit Congestion Notification (ECN)
494
4 Summary: TCP Variants for DCNs
503
5 Open Issues
505
6 Concluding Remarks
505
References
505
Routing Techniques in Data Center Networks
507
1 Introduction
507
2 Classification of Routing Schemes in Data Centers
510
2.1 Topology-Aware Routing
511
2.1.1 Server-Centric Approach
511
2.1.2 Switch-centric Approach
512
2.2 Energy-Aware Routing
516
2.2.1 Green Routing
516
2.2.2 Power-Aware Routing
518
2.3 Traffic-sensitive Routing
519
2.3.1 DARD
520
2.3.2 Hedera
522
2.3.3 ESM: Multicast Routing for Data Centers
523
2.3.4 GARDEN
524
2.4 Routing for Content Distribution Networks (CDN)
525
2.4.1 Request-Routing in CDNs
526
2.4.2 Symbiotic Routing
527
2.4.3 fs-PGBR: A Scalable and Delay Sensitive Cloud Routing Protocol
528
2.5 Summary of All Routing and Forwarding Techniques
528
3 Open Issues and Challenges
529
4 Conclusions
530
References
531
Part III Cloud Computing
533
Auditing for Data Integrity and Reliability in Cloud Storage
534
1 Introduction
534
2 Information Auditing: Objective and Approaches
536
2.1 Definition of Information Auditing
536
2.2 Three Approaches of Information Auditing
537
3 Auditing for Data Integrity in Distributed Systems
538
3.1 Strategies of Auditing Data Integrity
538
3.2 Proof of Retrievability
539
3.3 Provable Data Possession
542
3.3.1 Preliminaries
543
3.3.2 Defining the PDP Protocol
544
3.3.3 The Secure PDP Scheme (S-PDP)
545
3.3.4 The Efficient PDP Scheme (E-PDP)
547
3.4 Compact Proof of Retrievability
547
3.4.1 System Model
547
3.4.2 Private Verification Construction
548
3.4.3 Public Verification Construction
549
4 Auditing in Cloud Storage Platform
550
4.1 Challenges
551
4.2 Public Verifiability
552
4.3 Dynamic Data Operations Support
552
4.4 Privacy Preserving
554
4.5 Multiple Verifications
555
5 Open Questions
556
6 Conclusions
557
References
557
I/O and File Systems for Data-Intensive Applications
559
1 Parallel File Systems vs. Data-Intensive File Systems: A Comparison
559
2 Chunk-Aware I/O: Enabling HPC on Data-Intensive File Systems
562
2.1 Motivation
562
2.2 Chunk-Aware I/O Design
564
2.3 Chunk-Aware I/O Implementation
569
2.4 Chunk-Aware I/O Analysis
569
2.5 CHAIO Performance
570
2.5.1 Experiment Setup
570
2.5.2 Performance with Different Request Sizes
570
2.5.3 Performance with Two Replicas
571
2.5.4 Performance with Different Number of Nodes
572
2.5.5 Overhead Analysis in Large-Scale Computing Environments
573
2.5.6 Load Balance
575
3 Related Works
575
3.1 HPC on Data-Intensive File Systems
576
3.2 N-1 Data Access and its Handling
577
4 Summary
578
References
579
Cloud Resource Pricing Under Tenant Rationality
581
1 Introduction
581
2 The Game Model
582
2.1 User Model and Virtual Instances Pricing
582
2.2 Modeling Cloud Revenue and Tenant Surplus
583
2.2.1 Stage I: Cloud Revenue Maximization
583
2.2.2 Stage II: Tenant Surplus Maximization
584
2.3 Stackelberg Equilibrium
584
3 Usage-Based Cloud Resource Pricing
585
3.1 Non-Uniform Pricing
585
3.1.1 Stage II: Tenant Surplus Maximization
585
3.1.2 Stage I: Cloud Pricing Choices
586
3.2 Uniform Pricing
590
3.2.1 Stage II: Tenant Surplus Maximization
590
3.2.2 Stage I: Cloud Pricing Choices
591
4 The Effectiveness of Stackelberg Strategies
592
4.1 Centralized Aggregate Network Utility Maximization
592
4.2 Total Network Utility Under Selfish Interactions
595
4.3 Asymptotic Analysis of Price of Anarchy
597
5 Broker Resource Pricing
598
6 Performance Evaluation
600
6.1 Setup
600
6.2 Economic Implications of Cloud Resource Pricing
600
6.3 Social Welfare Tradeoffs, and Hidden Effects
601
7 Related Work
602
8 Concluding Remarks
603
References
603
Online Resource Management for Carbon-Neutral Cloud Computing
604
1 Introduction
604
1.1 Background
605
1.2 Carbon Neutrality: Benefits and Challenges
606
1.3 Current Research and Limitations
606
1.4 Contributions
607
2 Model
608
2.1 Some Assumptions
609
2.2 Energy Sources
609
2.3 Data Center
610
2.4 Workload
611
3 Problem Formulation
612
3.1 Objective and Constraints
612
3.2 Offline Problem Formulation
614
4 Algorithm for Cost Optimization and Carbon Neutrality
614
4.1 Carbon Deficit Queue
614
4.2 Optimizing for Cost Minimization and Carbon Neutrality
615
4.2.1 Working Principle of COCA
615
4.2.2 Distributed Implementation
616
4.3 Performance Analysis
617
5 Simulation
619
5.1 Data Sets
619
5.2 Results
621
5.2.1 Efficiency of COCA
621
5.2.2 Comparison with Prediction-Based Method
623
6 Extension to Geographic Load Balancing
624
7 Conclusions
625
References
625
A Big Picture of Integrity Verification of Big Data in Cloud Computing
628
1 Introduction
628
2 Motivating Examples
630
3 Problem Analysis---Framework and Lifecycle
631
4 Representative Approaches and Analysis
633
4.1 Preliminaries
633
4.1.1 RSA Signature
633
4.1.2 Bilinear Pairing and BLS Signature
634
4.1.3 Merkle Hash Tree
634
4.2 Representative Schemes
635
4.2.1 PDP
635
4.2.2 Compact POR
636
4.2.3 DPDP
637
4.2.4 Public Auditing of Dynamic Data
637
4.2.5 Authorized Auditing with Fine-Grained Data Updates
638
5 Other Related Work
638
6 Conclusions and Future Work
639
References
640
An Out-of-Core Task-based Middleware for Data-Intensive Scientific Computing
643
1 Introduction
643
2 Related Work
646
3 An Out-of-Core Task-based Middleware
647
3.1 Global and Local Schedulers
649
3.2 Storage Service
650
4 Linear Algebra Frontend (LAF)
651
5 A Case Study: Block Iterative Eigensolver Using DOoC+LAF
652
5.1 Eigenvalue Problem in the Configuration Interaction Approach
652
5.2 Implementation Using 1D partitioning
654
5.3 Implementation Using a 2D Partitioning
656
6 Experiments
656
6.1 Practical Considerations
657
6.2 Performance Results for Nmax=8
658
7 Conclusions
660
References
661
Building Scalable Software for Data Centers: An Approach to Distributed Computing at Enterprise Level
664
1 Introduction to Big Data Problems
664
2 Known Solutions at Design Phase: Overview of Design Patterns for Parallel & Distributed Computing
666
3 Introduction to MapReduce Programming Model
669
4 Overview of Apache Hadoop: A Framework for Distributed Computing
672
4.1 Distributed File System: HDFS
672
4.2 MapReduce Framework & API
674
4.3 Database Support: HBase
678
4.4 High Level Programming Language: Pig
679
4.5 Hive: Another Database Support & High Level Programming Language
680
5 Conclusions
682
References
682
Cloud Storage over Multiple Data Centers
685
1 Introduction
685
2 Cloud Storage in a Nutshell
687
2.1 Architecture
687
2.2 Metadata Service
689
2.2.1 Layout Manager
689
2.2.2 Meta-Server
689
2.2.3 Lock Service
690
2.3 Storage Service
690
2.3.1 Namenode
690
2.3.2 Chunk Servers
691
3 Replication Strategies
691
3.1 Introduction
691
3.2 Asynchronous Replication
692
3.3 Synchronous Replication
694
3.4 Placement of Replicas
695
4 Data Striping Methods
696
4.1 Introduction
696
4.2 Erasure Code Types
697
4.3 Erasure Codes in Data Centers
698
5 Consistency Models
699
5.1 Introduction
699
5.2 Strong Consistency
700
5.3 Weak Consistency
701
6 Cloud of Multiple Clouds
703
6.1 Introduction
703
6.2 Architecture
704
6.3 Data Striping
705
6.4 Retrieving Strategy
707
6.5 Mutual Exclusion
707
7 Privacy and Security of Storage System
709
7.1 Introduction
709
7.2 Fine-Grained Data Access Control
710
7.3 Security on Storage Server
712
8 Conclusion and Future Directions
714
References
715
Part IV Hardware
720
Realizing Accelerated Cost-Effective Distributed RAID
721
1 Introduction
721
2 Background
723
2.1 Rationale
723
2.1.1 Backend vs. Client-driven Parity Generation
723
2.1.2 Block-Based vs. Per-File RAID
724
2.1.3 Hardware vs. Accelerated Software RAID
724
2.1.4 Discussion
725
2.2 Enabling Technologies
725
2.2.1 Erasure Codes
725
2.2.2 The Lustre Parallel File System
727
2.2.3 KGPU
727
3 Design
728
3.1 System Overview
728
3.2 RAID-enabled PFS Design
729
3.3 Control Flow
730
3.4 Degraded Array Reconstruction
732
4 Implementation
732
4.1 Basic GPU Implementation
733
4.2 Optimizations
733
5 Evaluation
734
5.1 Experimental Setup
734
5.2 I/O Throughput Measurement
735
5.2.1 Raw Throughput
735
5.2.2 Encoding Throughput
736
5.2.3 Impact of Number of Disks on Throughput
737
5.2.4 End-to-End Data Integrity
739
5.3 RAID Reconstruction Cost
739
5.4 Impact on Applications
740
6 Related Work
740
7 Conclusion
742
References
742
Efficient Hardware-Supported Synchronization Mechanisms for Manycores
745
1 Introduction
745
2 The G-Lines Technology
746
3 Hardware Barrier Synchronization
747
4 The GBarrier Synchronization Mechanism
748
4.1 Dedicated On-Chip Network Architecture
749
4.2 Synchronization Protocol
750
4.3 Programmability Issues
753
5 Performance Implications
754
5.1 Implementation Technologies
754
5.1.1 G-Lines Technology
754
5.1.2 Standard Technology
754
5.2 Raw Performance Statistics
755
6 Evaluation
757
6.1 Experimental Setup
757
6.2 Barrier Implementations
758
6.3 Performance Results
759
6.3.1 Execution Time
759
6.3.2 Network Traffic
763
6.3.3 Energy Efficiency
765
7 Related Work
766
8 Hardware Lock Synchronization
768
9 The GLock Synchronization Mechanism
770
9.1 Dedicated On-Chip Network Architecture
770
9.2 Synchronization Protocol
771
9.3 Programmability Issues
774
10 Performance Implications
776
10.1 Implementation Technologies
776
10.1.1 G-Lines Technology
776
10.1.2 Standard Technology
777
10.2 Raw Performance Statistics
778
11 Evaluation
779
11.1 Experimental Setup
779
11.2 Post-mortem Analysis of Benchmarks
781
11.3 Lock Implementations
782
11.4 Performance Results
783
11.4.1 Execution Time
783
11.4.2 Network Traffic
786
11.4.3 Energy Efficiency
788
12 Related Work
789
13 Conclusions
791
References
793
Hardware Approaches to Transactional Memory in Chip Multiprocessors
796
1 Introduction
796
2 Why Transactional Memory Is Going Mainstream
798
2.1 The Drawbacks of Lock-Based Synchronization
799
2.2 The Transactional Abstraction
799
2.3 High-Performance Transactional Memory
800
2.4 Industrial Adoption of Hardware Transactional Memory
801
3 Fundamentals of Transactional Memory
802
4 Hardware Mechanisms for Transactional Memory
803
4.1 ISA Extensions
803
4.2 Transactional Book-Keeping
804
4.3 Data Versioning
805
4.4 Conflict Detection and Resolution
805
4.5 Transaction Commit
807
4.6 Transaction Abort
807
5 Intel TSX: TM Support in Mainstream Processors
808
5.1 Hardware Lock Elision
809
5.2 Restricted Transactional Memory
810
6 Analysing Intel TSX Performance on Haswell
810
7 An Overview of Hardware TM Research
815
8 Conclusions
821
References
821
Part V Modeling and Simulation
827
Data Center Modeling and Simulation Using OMNeT++
828
1 Introduction to Modeling and Simulation (M&S) Methodology
829
1.1 Parallel Discrete Event Simulation---PDES
830
2 Data Center Architectures
831
3 Data Center Modeling Using OMNeT++
833
3.1 Simple Two Node Simulation
833
3.2 Advance Level Simulation
836
3.3 Data Center Simulation Model
839
4 Wrap Up
843
References
843
Power-Thermal Modeling and Control of Energy-Efficient Servers and Datacenters
845
1 Introduction
845
1.1 Overall Datacenter Architecture
847
1.2 Datacenter Workload Characteristics
848
1.3 Energy Efficiency of Datacenters
850
1.4 Chapter Organization
851
2 State-of-the-Art in Datacenter Design
852
2.1 Computing Servers
852
2.2 Cooling Infrastructure
854
3 Power and Temperature Modeling and Monitoring
857
3.1 Server Modeling
858
3.2 Datacenter Modeling
861
3.3 Monitoring System for Datacenters
863
4 Power and Thermal Managements of Servers
864
4.1 Overview of CPU Power and Thermal Management Techniques
865
4.2 Run-Time Hierarchical Power and Thermal Management for Server Architectures
867
4.3 Design-Time Power and Thermal Optimizations
871
5 Power and Thermal Managements for Server Clusters
876
5.1 Conventional Solution to Minimize Power Consumption for Server Clusters
876
5.2 Correlation-Aware Power and Temperature Management
877
6 Power Minimization of Datacenters with Hybrid Cooling Architectures
886
6.1 Formal Problem Definition
888
6.2 Multi-objective Trade-offs Exploration Between Cooling Mode and Utilization Threshold
889
6.3 Simulation Results
893
7 Conclusions
895
References
896
Thermal Modeling and Management of Storage Systems in Data Centers
902
1 Introduction
902
2 Related Work
904
2.1 Efficient Data Centers
904
2.2 Thermal Modeling
905
2.3 Thermal Management
905
3 Thermal Modeling
906
3.1 CPU Thermal Model
907
3.2 Disk Thermal Model
909
3.3 Thermal Model of Data Nodes
911
3.4 Evaluation of Temperature Models
912
4 Thermal Management Strategies
913
4.1 Task Scheduling
914
4.2 Predictive Thermal-Aware Data Transmission
917
5 Results
919
5.1 Task Scheduling
919
5.1.1 CPU-Intensive Workload
920
5.1.2 I/O-Intensive Workloads
922
5.2 Predictive Thermal-Aware Management System
922
6 Conclusion
926
References
927
Modeling and Simulation of Data Center Networks
931
1 Data Centers and Cloud Computing
931
2 DCN Architectures
933
3 DCN Graph Modeling
935
3.1 ThreeTier DCN Model
936
3.2 FatTree DCN Model
937
3.3 DCell DCN Model
938
4 DCNs Implementation in ns-3
939
4.1 ThreeTier DCN Implementation Details
939
4.2 FatTree DCN Implementation Details
940
4.3 DCell DCN Implementation Details
942
References
944
Part VI Security
945
C2Hunter: Detection and Mitigation of Covert Channels in Data Centers
946
1 Introduction
946
2 Background
949
3 Threat Model, Scenarios and Assumptions
950
3.1 Threat of Data Center
950
3.2 Threat Categories of Covert Channels
951
3.3 Threat Scenarios of Covert Channels
952
3.4 Assumptions
953
4 Overview of C2Hunter
953
4.1 Challenges
953
4.2 Formal Requirements
954
4.3 C2Hunter Framework Summary
954
4.4 Covert Channel Modeling
956
5 Two-Phase Synthesis Detection Algorithm
958
5.1 Markov Detection Algorithm
959
5.2 Bayesian Detection Algorithm
962
6 Mitigation Algorithm
963
7 Implementation and Evaluation
964
7.1 Covert Channels Scenarios
965
7.2 Captor and Detector
966
7.3 Interrupter in Hypervisor
967
7.4 Experimental Settings
967
7.5 Detection Analysis
969
7.6 Mitigation Analysis
972
8 Discussion
974
9 Related Work
976
10 Conclusion
977
References
978
Selective and Private Access to Outsourced Data Centers
982
1 Introduction
982
2 Access Control Enforcement
984
2.1 Selective Encryption
984
2.2 Updates to the Access Control Policy
988
2.3 Write Privileges
992
2.4 Attribute-Based Encryption
994
3 Efficient Access to Encrypted Data
995
4 Protecting Access Privacy
998
4.1 Oblivious RAM
999
4.2 Dynamically Allocated Data Structures
1000
4.3 Shuffle Index
1002
5 Combining Access Control and Indexing Techniques
1007
6 Conclusions
1010
References
1010
Privacy in Data Centers: A Survey of Attacks and Countermeasures
1013
1 Introduction
1013
2 Privacy
1015
3 Privacy Enhancing Technologies
1016
4 Anonymous Communications
1017
5 Mix Networks
1019
6 Traffic Analysis
1019
7 Mix Systems Attacks
1020
8 The Disclosure Attack
1020
9 The Statistical Disclosure Attack (SDA)
1021
10 Extending and Resisting Statistical Disclosure
1022
11 Two Sided Statistical Disclosure Attack (TS-SDA)
1022
12 Perfect Matching Disclosure Attack (PMDA)
1023
13 Vida: How to Use Bayesian Inference to De-anonymize Persistent Communications
1024
14 SDA with Two Heads (SDA-2H)
1024
15 Conclusions
1025
References
1025
Part VII Data Services
1028
Quality-of-Service in Data Center Stream Processing for Smart City Applications
1029
1 Introduction
1029
2 Distributed Stream Processing Systems
1030
2.1 Abstract Model
1031
2.2 Development Model
1033
2.3 Execution Model
1034
3 Platforms for Distributed Stream Processing
1036
3.1 IBM InfoSphere Streams
1036
3.2 Apache S4
1037
3.3 Storm
1038
4 QoS-Aware Stream Processing
1039
5 Quasit
1041
5.1 Quasit Abstract Model
1042
5.2 Quasit Development Model
1043
5.3 Quasit Execution Model
1048
6 Load-Adaptive Active Replication (LAAR)
1049
7 Conclusions
1054
References
1055
Opportunistic Databank: A context Aware on-the-fly Data Center for Mobile Networks
1059
1 Introduction
1059
2 Data Replication in Manets---A Brief Overview
1062
3 Data Replication in DTNs
1064
3.1 System Model
1065
3.2 Hybrid Scheme for Message Replication (HSM) for DTNs
1067
3.3 Empirical Setups and Results
1069
3.3.1 Performance Metrics
1070
3.3.2 Related DTN Replication Schemes
1071
3.3.3 Simulation Results
1072
4 Conclusions
1074
References
1074
Data Management: State-of-the-Practice at Open-Science Data Centers
1077
1 Introduction
1077
2 Data Storage Infrastructure
1079
2.1 Data Storage Media
1079
2.2 General Architecture of a Data Storage System
1080
2.3 Supporting Databases for Structured and Semi-Structured Datasets
1080
2.4 Examples of Notable Storage Systems at Open-Science Data Centers
1081
3 Data Movement
1082
3.1 Parallel File-System Associated with Computational Resources---Secondary Storage
1082
3.2 Optimizing Data Movement in Context of Secondary Storage System
1085
3.3 Optimizing Data Movement in Context of Tertiary Storage System
1086
4 Data Archiving
1087
5 Data Preservation
1088
6 Conclusion
1089
References
1089
Data Summarization Techniques for Big Data---A Survey
1091
1 Introduction
1091
2 Applications of Data Summarization
1093
3 Clustering Algorithms
1095
3.1 Background
1095
3.2 Hierarchical Clustering
1097
3.3 Partitioning Clustering
1101
3.4 Density-Based Clustering Algorithms
1103
3.5 Grid-Based Clustering Algorithms
1105
4 Sampling
1107
4.1 Probability Sampling
1108
4.2 Non-Probabilistic Sampling
1114
5 Compression
1115
6 Wavelets
1120
7 Histograms
1123
8 Micro-Clustering
1125
9 Conclusion
1126
References
1126
Part VIII Monitoring
1135
Central Management of Datacenters
1136
1 Introduction
1136
2 Organization of the Chapter
1137
2.1 Management Layer Network
1137
2.2 Provisioning of Servers
1139
2.2.1 Reason to Use Provisioning Servers
1139
2.3 Platform Configuration Management System
1140
2.4 Resource Utilization Monitoring
1140
2.5 Alerting and Alarming System
1142
2.6 Central Logging System
1142
2.6.1 Security Information Event Management
1144
2.7 Intrusion Detection and Prevention System
1144
2.7.1 Types of Intrusion Detection System (IDS)
1145
Network-Based Intrusion Detection System (NIDS)
1146
Host-Based Intrusion Detection System (HIDS)
1146
2.7.2 How Intrusion Detection System Works?
1146
Anomaly-Based Intrusion Detection System
1146
Signature-Based Intrusion Detection System
1146
2.8 Datacenter Backup and Restore
1147
2.8.1 The Components of Data Backup and Recovery
1148
Cold and Hot Backup
1148
Enterprise Backup and Restore Software
1148
Online and Offline Storage
1148
2.9 Security Management Systems
1149
3 Conclusion
1149
References
1151
Monitoring of Data Centers using Wireless Sensor Networks
1152
1 Introduction
1152
2 Survey Study
1155
3 Conclusion
1163
References
1163
Network Intrusion Detection Systems in Data Centers
1165
1 Introduction
1165
2 Origin and Standardization
1170
3 Architecture
1172
4 Subjects of Study
1175
5 Detection Strategies
1177
6 Alert Correlation
1181
7 Summary
1183
References
1184
Software Monitoring in Data Centers
1188
1 Introduction
1188
1.1 Performance Degradation
1189
1.2 Function Failure
1190
1.3 Energy Conservation
1191
2 Monitoring Content
1192
2.1 Basic Software
1193
2.2 Middleware
1193
2.3 Database
1194
2.4 Application Software
1194
2.5 PM (Physical Machine) and VM (Virtual Machine)
1196
2.6 User Behavior Analysis
1198
2.7 Hot-Spot Evaluation
1198
2.8 Performance Prediction and Advanced Warning
1200
2.9 The Performance Bottlenecks Analysis
1201
3 Monitoring Timing
1202
3.1 Resource-Oriented Monitoring
1202
3.2 Business-Oriented Monitoring
1205
4 Participators
1207
4.1 Resource Managers
1207
4.2 Service Operators
1208
4.3 Data Owner
1209
4.4 Software Developers
1209
5 Monitoring Site
1210
5.1 On-Site Monitor
1211
5.2 Off-Site Monitor
1211
6 Monitoring Methods
1212
6.1 Visualization Monitoring
1212
6.2 Hot-Spot Evaluation
1214
6.3 Performance Prediction
1220
6.4 Analyzing User's Habits
1226
6.5 Tools
1227
References
1229
Part IX Resource Management
1233
Usage Patterns in Multi-tenant Data Centers: a Large-Case Field Study
1234
1 Introduction
1234
2 Multi-tenant Datacenters
1236
2.1 Evolution of Resource Demands
1236
2.2 CPU Load Balancing
1237
2.3 The Impact of Time Scales
1240
3 Summary
1242
References
1242
On Scheduling in Distributed Transactional Memory: Techniques and Tradeoffs
1244
1 Introduction
1244
2 Preliminaries and System Model
1246
2.1 Distributed Transactions
1246
2.2 Definitions
1247
2.3 Transactional Scheduler
1247
3 Bi-interval
1248
3.1 Motivation
1248
3.2 Scheduler Design
1249
3.3 Analysis
1250
3.4 Evaluation
1252
4 Cluster-Based Transactional Scheduler
1253
4.1 Motivation
1253
4.2 Scheduler Design
1254
4.3 Analysis
1256
4.4 Evaluation
1257
5 Summary and Conclusion
1258
References
1259
Dependability-Oriented Resource Management Schemes for Cloud Computing Data Centers
1261
1 Introduction
1261
2 System Model and Failure Behavior of Data Center Components
1262
2.1 Overview of the Data Center Architecture
1262
2.2 Failure Behavior of Servers
1263
2.3 Failure Behavior of Network Components
1264
2.4 Analysis of the Impact of Failures on Applications
1265
3 Resource Management in Data Center Environments
1266
3.1 Global Constraints
1268
3.2 Infrastructure-Oriented Constraints
1269
3.3 Application-Oriented Constraints
1270
4 Initial Allocation of Virtual Machines in Data Center Environments
1271
4.1 A Comprehensive Scheme for Virtual Machines Allocation
1271
4.2 Other Schemes for Virtual Machines Allocation
1273
5 Runtime Adaption of Virtual Machine Allocation in Data Center Environments
1275
5.1 Runtime Adaption to Balance Availability and Performance
1276
5.2 Other Schemes for Runtime Virtual Machines Allocation Adaption
1277
6 Conclusions
1279
References
1279
Resource Scheduling in Data-Centric Systems
1282
1 Introduction
1282
2 Terminology
1284
3 Classification and State-of-the-Art
1285
3.1 Hierarchy of Resource Scheduling in DCS
1285
3.2 Resource Provision
1287
3.2.1 Economic-Based Resource Provision
1287
3.2.2 SLA-Oriented Resource Provision
1288
3.2.3 Utility-Oriented Resource Provision
1288
3.3 Job Scheduling
1289
3.3.1 Static Job Scheduling
1290
3.3.2 Dynamic Job Scheduling
1290
3.4 Data Scheduling
1292
3.4.1 Online Data Scheduling
1293
3.4.2 Offline Data Scheduling
1293
4 Case Studies
1294
4.1 Amazon EC2
1294
4.2 Dawning Nebulae
1295
4.3 Taobao Yunti
1296
4.4 Microsoft SCOPE
1297
5 Future Trends and Challenges
1298
6 Conclusions
1299
References
1300
Index
1306
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