Modeling and optimization of parallel and distributed embedded systems

510 Pages·2016·13.697 MB·English

by  Gordon-Ross, Ann, Munir, Arslan, Ranka, Sanjay

Table of Contents
Cover
Title Page
Copyright
Dedication
Preface
About This Book
Highlights
Intended Audience
Organization of the Book
Acknowledgment
Part One: Overview
Chapter 1: Introduction
1.1 Embedded Systems Applications
1.2 Characteristics of Embedded Systems Applications
1.3 Embedded Systems—Hardware and Software
1.4 Modeling—An Integral Part of the Embedded Systems Design Flow
1.5 Optimization in Embedded Systems
1.6 Chapter Summary
Chapter 2: Multicore-Based EWSNs—An Example of Parallel and
Distributed Embedded Systems
2.1 Multicore Embedded Wireless Sensor Network Architecture
2.2 Multicore Embedded Sensor Node Architecture
2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs
2.4 MCEWSN Application Domains
2.5 Multicore Embedded Sensor Nodes
2.6 Research Challenges and Future Research Directions
2.7 Chapter Summary
Part Two: Modeling
Chapter 3: An Application Metrics Estimation Model for Embedded
Wireless Sensor Networks
3.1 Application Metrics Estimation Model
3.2 Experimental Results
3.3 Chapter Summary
Chapter 4: Modeling and Analysis of Fault Detection and Fault Tolerance in
Embedded Wireless Sensor Networks
4.1 Related Work
4.2 Fault Diagnosis in WSNs
4.3 Distributed Fault Detection Algorithms
4.4 Fault-Tolerant Markov Models
4.5 Simulation of Distributed Fault Detection Algorithms
4.6 Numerical Results
4.7 Research Challenges and Future Research Directions
4.8 Chapter Summary
Chapter 5: A Queueing Theoretic Approach for Performance Evaluation of
Low-Power Multicore-Based Parallel Embedded Systems
5.1 Related Work
5.2 Queueing Network Modeling of Multicore Embedded Architectures
5.3 Queueing Network Model Validation
5.4 Queueing Theoretic Model Insights
5.5 Chapter Summary
Part Three: Optimization
Chapter 6: Optimization Approaches in Distributed Embedded Wireless
Sensor Networks
6.1 Architecture-Level Optimizations
6.2 Sensor Node Component-Level Optimizations
6.3 Data Link-Level Medium Access Control Optimizations
6.4 Network-Level Data Dissemination and Routing Protocol
Optimizations
6.5 Operating System-Level Optimizations
6.6 Dynamic Optimizations
6.7 Chapter Summary
Chapter 7: High-Performance Energy-Efficient Multicore-Based Parallel
Embedded Computing
7.1 Characteristics of Embedded Systems Applications
7.2 Architectural Approaches
7.3 Hardware-Assisted Middleware Approaches
7.4 Software Approaches
7.5 High-Performance Energy-Efficient Multicore Processors
7.6 Challenges and Future Research Directions
7.7 Chapter Summary
Chapter 8: An MDP-Based Dynamic Optimization Methodology for
Embedded Wireless Sensor Networks
8.1 Related Work
8.2 MDP-Based Tuning Overview
8.3 Application-Specific Embedded Sensor Node Tuning Formulation
as an MDP
8.4 Implementation Guidelines and Complexity
8.5 Model Extensions
8.6 Numerical Results
8.7 Chapter Summary
Chapter 9: Online Algorithms for Dynamic Optimization of Embedded
Wireless Sensor Networks
9.1 Related Work
9.2 Dynamic Optimization Methodology
9.3 Experimental Results
9.4 Chapter Summary
Chapter 10: A Lightweight Dynamic Optimization Methodology for
Embedded Wireless Sensor Networks
10.1 Related Work
10.2 Dynamic Optimization Methodology
10.3 Algorithms for Dynamic Optimization Methodology
10.4 Experimental Results
10.5 Chapter Summary
Chapter 11: Parallelized Benchmark-Driven Performance Evaluation of
Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel
Embedded Systems
11.1 Related Work
11.2 Multicore Architectures and Benchmarks
11.3 Parallel Computing Device Metrics
11.4 Results
11.5 Chapter Summary
Chapter 12: High-Performance Optimizations on Tiled Manycore
Embedded Systems: A Matrix Multiplication Case Study
12.1 Related Work
12.2 Tiled Manycore Architecture (TMA) Overview
12.3 Parallel Computing Metrics and Matrix Multiplication (MM) Case
Study
12.4 Matrix Multiplication Algorithms' Code Snippets for Tilera's
TILEPro64
12.5 Performance Optimization on a Manycore Architecture
12.6 Results
12.7 Chapter Summary
Chapter 13: Conclusions
References
Index
End User License Agreement
List of Illustrations
Chapter 2: Multicore-Based EWSNs—An Example of Parallel and Distributed
Embedded Systems
Figure 2.1 A heterogeneous multicore embedded wireless sensor network
(MCEWSN) architecture
Figure 2.2 Multicore embedded sensor node architecture
Figure 2.3 Omnibus sensor information fusion model for an MCEWSN
architecture
Chapter 4: Modeling and Analysis of Fault Detection and Fault Tolerance in
Embedded Wireless Sensor Networks
Figure 4.1 Wireless sensor network architecture
Figure 4.2 Byzantine faulty behavior in WSNs
Figure 4.3 Various types of sensor faults [93]: (a) outlier faults; (b) stuck-at
faults; (c) noisy faults
Figure 4.4 A non-FT (NFT) sensor node Markov model
Figure 4.5 FT sensor node Markov model [130]
Figure 4.6 WSN cluster Markov model [130]
Figure 4.7 WSN cluster Markov model with three states [130]
Figure 4.8 WSN Markov model [130]
Figure 4.9 The ns 2-based simulation architecture
Figure 4.10 Effectiveness and false positive rate of the Chen algorithm: (a)
error detection accuracy for the Chen algorithm; (b) false positive rate of
Chen algorithm
Figure 4.11 Effectiveness and false positive rate for the Ding algorithm: (a)
error detection accuracy for the Ding algorithm; (b) false positive rate for
the Ding algorithm
Figure 4.12 Noise power levels in the Intel Berkeley sample
Figure 4.13 Distribution of constant error occurrences
Figure 4.14 Error detection and false positive rate for the Chen algorithm
using real-world data
Figure 4.15 Error detection and false positive rate for the Ding algorithm
using real-world data
Figure 4.16 MTTF in days for an NFT and an FT sensor node [130]
Figure 4.17 MTTF in days for an NFT WSN cluster and an FT WSN cluster
with [130]
Figure 4.18 MTTF in days for an NFT WSN and an FT WSN with
[130]
Chapter 5: A Queueing Theoretic Approach for Performance Evaluation of
Low-Power Multicore-Based Parallel Embedded Systems
Figure 5.1 Queueing network model for the 2P-2L1ID-2L2-1M multicore
embedded architecture
Figure 5.2 Queueing network model for the 2P-2L1ID-1L2-1M multicore
embedded architecture
Figure 5.3 Queueing network model validation of the response time in ms
for mixed workloads for 2P-2L1ID-1L2-1M for a varying number of jobs
Figure 5.4 Flow chart for our queueing network model setup in SHARPE
Figure 5.5 The effects of cache miss rate on response time (ms) for mixed
workloads for 2P-2L1ID-2L2-1M for a varying number of jobs : (a)
relatively low cache miss rates; (b) relatively high cache miss rates
Figure 5.6 The effects of processor-bound workloads on response time (ms)
for 2P-2L1ID-2L2-1M for a varying number of jobs for cache miss rates:
L1-I = 0.01, L1-D = 0.13, and L2 = 0.3: (a) processor-bound workloads
(processor-to-processor probability ); (b) processor-bound
workloads (processor-to-processor probability )
Chapter 6: Optimization Approaches in Distributed Embedded Wireless
Sensor Networks
Figure 6.1 Embedded wireless sensor network architecture
Figure 6.2 Embedded sensor node architecture with tunable parameters
Figure 6.3 Data aggregation
Figure 6.4 Directed diffusion: (a) interest propagation, (b) initial gradient
setup, and (c) data delivery along the reinforced path
Chapter 7: High-Performance Energy-Efficient Multicore-Based Parallel
Embedded Computing
Figure 7.1 High-performance energy-efficient parallel embedded
computing (HPEPEC) domain
Figure 7.2 Classification of optimization techniques based on embedded
application characteristics
Chapter 8: An MDP-Based Dynamic Optimization Methodology for Embedded
Wireless Sensor Networks
Figure 8.1 Process diagram for our MDP-based application-oriented
dynamic tuning methodology for embedded wireless sensor networks
Figure 8.2 Reward functions: (a) power reward function ; (b)
throughput reward function ; (c) delay reward function
Figure 8.4 Symbolic representation of our MDP model with four sensor
node states
Figure 8.3 Reliability reward functions: (a) linear variation; (b) quadratic
variation
Figure 8.5 The effects of different discount factors on the expected total
discounted reward for a security/defense system. if ,
Figure 8.6 Percentage improvement in expected total discounted reward
for for a security/defense system as compared to the fixed heuristic
policies. if ,
Figure 8.7 The effects of different state transition costs on the expected
total discounted reward for a security/defense system. ,
Figure 8.8 The effects of different reward function weight factors on the
expected total discounted reward for a security/defense system. ,
if
Figure 8.9 The effects of different discount factors on the expected total
discounted reward for a healthcare application. if ,
Figure 8.10 Percentage improvement in expected total discounted reward
for for a healthcare application as compared to the fixed heuristic
policies. if ,
Figure 8.11 The effects of different state transition costs on the expected
total discounted reward for a healthcare application. ,
Figure 8.12 The effects of different reward function weight factors on the
expected total discounted reward for a healthcare application. ,
if
Figure 8.13 The effects of different discount factors on the expected total
discounted reward for an ambient conditions monitoring application.
if ,
Figure 8.14 Percentage improvement in expected total discounted reward
for for an ambient conditions monitoring application as compared to
the fixed heuristic policies. if ,
Figure 8.15 The effects of different state transition costs on the expected
total discounted reward for an ambient conditions monitoring application.
,
Figure 8.16 The effects of different reward function weight factors on the
expected total discounted reward for an ambient conditions monitoring
application. , if
Chapter 9: Online Algorithms for Dynamic Optimization of Embedded
Wireless Sensor Networks
Figure 9.1 Dynamic optimization methodology for distributed EWSNs
Figure 9.2 Lifetime objective function
Figure 9.3 Objective function value normalized to the optimal solution for a
varying number of states explored for the greedy and simulated annealing
algorithms for a security/defense system where , , ,
Figure 9.4 Objective function value normalized to the optimal solution for a
varying number of states explored for the greedy and simulated annealing
algorithms for a healthcare application where , , ,
Figure 9.5 Objective function value normalized to the optimal solution for a
varying number of states explored for the greedy and simulated annealing
algorithms for an ambient conditions monitoring application where ,
, ,
Figure 9.6 Data memory requirements for exhaustive search, greedy, and
simulated annealing algorithms for design space cardinalities of 8, 81, 729,
and 46,656
Chapter 10: A Lightweight Dynamic Optimization Methodology for Embedded
Wireless Sensor Networks
Figure 10.1 A lightweight dynamic optimization methodology per sensor
node for EWSNs
Figure 10.2 Lifetime objective function
Figure 10.3 Objective function value normalized to the optimal solution for
a varying number of states explored for one-shot, greedy, and SA
algorithms for a security/defense system where , , ,
and
Figure 10.4 Objective function value normalized to the optimal solution for
a varying number of states explored for one-shot, greedy, and SA
algorithms for a security/defense system where , , ,
and
Figure 10.5 Objective function value normalized to the optimal solution for
a varying number of states explored for one-shot, greedy, and SA