
Computer Performance Engineering
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Content
- Intro
- Preface
- Organization
- Contents
- Applications I
- A Markovian Queueing System for Modeling a Smart Green Base Station
- 1 Introduction
- 2 The Model
- 3 Process of the System State
- 4 Stationary Solution
- 4.1 Algorithms for Stationary Probabilities
- 4.2 Performance Metrics
- 4.3 Numerical Example
- 5 First Passage Times
- 6 An Approximated Product-form Model
- 7 Conclusion
- References
- Static and Dynamic Hosting of Cloud Servers
- 1 Introduction
- 2 Static and Dynamic Policies
- 2.1 Queued Jobs
- 3 Virtual Machines Do Not Move
- 4 Conclusion
- References
- Security and Performance Tradeoff Analysis of Mobile Offloading Systems Under Timing Attacks
- 1 Introduction
- 2 System Overview and The Model
- 2.1 Behavior of System and Attackers
- 2.2 The System Model
- 3 Metrics
- 3.1 Security Metrics
- 3.2 Performance Metrics
- 3.3 Tradeoff Metric
- 4 Model Analysis
- 4.1 CTMC Steady-State Probability Computation
- 4.2 CTMC with Absorbing State - MTTSF Analysis
- 4.3 Throughput Analysis
- 5 Numerical Results
- 6 Conclusion and Future Work
- References
- Non-Markovian Performability Evaluation of ERTMS/ETCS Level 3
- 1 Introduction
- 2 The ERTMS/ETCS L3 Case-Study
- 3 Non-Markovian Modeling and Analysis
- 3.1 Stochastic Time Petri Nets
- 3.2 Regenerative Transient Analysis Through Stochastic State Classes
- 4 Performability Evaluation of ERTMS/ETCS Level 3
- 4.1 A Non-Markovian Model of Communication Availability
- 4.2 Evaluation of the Communication Availability Model
- 4.3 A Non-Markovian Model of ERTMS/ETCS Level 3
- 4.4 Evaluation of the ERTMS/ETCS Level 3 Model
- 5 Conclusions
- References
- Modelling Techniques I
- Simplifying Layered Queuing Network Models
- 1 Introduction
- 2 Related Work
- 3 The Layered Queuing Network (LQN) Model
- 4 The Simplification Process
- 4.1 Details of the Operations: Example 1
- 4.2 Details of the Operations: Example 2
- 5 Case Study
- 6 Conclusions
- References
- Moment-Generating Algorithm for Response Time in Processor Sharing Queueing Systems
- 1 Introduction
- 2 Background
- 2.1 Scheduling
- 2.2 PS Applications
- 2.3 Queueing Models
- 2.4 Response Times
- 3 Related Work on Response Times
- 4 Moment-Generating Algorithm
- 5 Multi-Class Algorithm
- 6 Case Studies
- 7 Conclusion and Future Work
- References
- On Generalized Processor Sharing and Objective Functions: Analytical Framework
- 1 Introduction
- 2 Analytical Framework
- 3 Some Illustrative Examples
- 4 Framework Application
- 5 Achieving a Specific Performance Vector
- 6 Conclusions
- References
- Software Performance
- Comparing the Accuracy of Resource Demand Measurement and Estimation Techniques
- 1 Introduction
- 2 Extracting Resource Demands
- 2.1 Performance Management Work - Tools Monitoring
- 2.2 Dynatrace Application Monitoring
- 2.3 Library for Resource Demand Estimation
- 3 Evaluation
- 3.1 Standalone Evaluation
- 3.2 Distributed Setup
- 4 Related Work
- 5 Conclusion and Future Work
- References
- Estimating the Impact of Code Additions on Garbage Collection Overhead
- 1 Introduction
- 2 Garbage Collection Essentials
- 3 Modeling Garbage Collection Overhead
- 3.1 Reconstructing Allocation Behavior
- 3.2 Considering Additional Allocations
- 3.3 Estimating Collection Time
- 4 Evaluation and Discussion
- 4.1 Methodology and Metrics
- 4.2 Workloads
- 4.3 Measurement Platform and Results
- 4.4 Results Discussion
- 5 Related Work
- 6 Conclusion
- References
- Performance Awareness in Java EE Development Environments
- 1 Introduction
- 2 Performance Awareness Approach
- 2.1 Developer Interaction
- 2.2 Model Creation
- 2.3 Data Collection and Aggregation
- 2.4 Performance Simulation
- 3 Evaluation
- 3.1 Experiment Setup
- 3.2 Experiment Results
- 4 Related Work
- 5 Conclusion and Future Work
- References
- Modelling Techniques II
- Canonical Form of Order-2 Non-stationary Markov Arrival Processes
- 1 Introduction
- 2 Theoretical Background
- 3 Previous Results for MAP(2) and RAP(2) Processes
- 4 Canonical Form of Order-2 NMAP
- 5 Equivalence of the NMAP(2) and NRAP(2) Classes
- 6 Conclusion
- References
- Markov Decision Petri Nets with Uncertainty
- 1 Objectives and Contributions
- 2 Background
- 3 Markov Decision Petri Net with Uncertainty
- 4 Case Study
- 5 Conclusions
- References
- On-the-fly Fluid Model Checking via Discrete Time Population Models
- 1 Introduction
- 2 Population Models
- 2.1 Continuous Time Population Models
- 2.2 Discrete Time Population Models
- 2.3 Relationship Between the Models
- 3 Properties of Individual Objects
- 4 Fluid Model Checking via Discrete Time Models
- 5 Benchmark Examples and Comparison
- 5.1 A Computer Worm Epidemic Model
- 5.2 A Client-Server Model
- 6 Related Work
- 7 Conclusions
- References
- Computing Response Time Distributions Using Iterative Probabilistic Model Checking
- 1 Introduction
- 2 Problem Statement and Case Study
- 2.1 Service-Oriented Systems
- 2.2 Performance Questions
- 3 A Formal Model for Service Systems
- 3.1 iDSL Language Syntax
- 3.2 iDSL Model of iXR Systems
- 3.3 Three Sampling Methods for Measurements
- 3.4 Performance Queries in iDSL
- 3.5 Translation to Modest
- 4 Computing Response Time Distributions
- 4.1 From iDSL Queries to Modest
- 4.2 Aggregating Latencies of Service Requests
- 4.3 Iterative Model Checking for Probability Bounds
- 4.4 Transforming Bounds into a Set of Possible CDFs
- 4.5 Answering the Performance Queries Using the CDFs
- 5 Case Study Results
- 6 Conclusion
- References
- Applications II
- iDSL: Automated Performance Prediction and Analysis of Medical Imaging Systems
- 1 Introduction
- 2 Methodology
- 3 Measurements and Emperical CDFs
- 3.1 Measuring Activities on a Real System
- 3.2 Grouping Activities into Execution Times
- 3.3 Using Execution Times to Estimate eCDFs
- 3.4 Predicting eCDFs for the Complete Design Space
- 4 Extending the iDSL Language and Solution Chain
- 4.1 The iDSL Model of iXR Systems
- 4.2 Automated Prediction of eCDFs for the Complete Design Space
- 4.3 Automated Aggregation of Latencies
- 5 Results of a Case Study on iXR Systems
- 5.1 The Performance of an iXR System
- 5.2 The Validity and Applicability of the iDSL Model
- 6 Conclusions and Future Work
- References
- Stream Processing on Demand for Lambda Architectures
- 1 Introduction
- 2 Stream Processing On Demand
- 2.1 Data Processing in the Lambda Architecture
- 2.2 Decision-Making Model
- 3 Experimental Validation
- 3.1 Use Case and Design Options
- 3.2 Implementation of the Batch Layer
- 3.3 Performance Model Prototype
- 3.4 Controlled Experiment
- 4 Related Work
- 5 Conclusion and Future Work
- References
- An AnyLogic Simulation Model for Power and Performance Analysis of Data Centres
- 1 Introduction
- 2 System Description
- 3 Data Center Models
- 3.1 Model Overview
- 3.2 IT Equipment Model
- 3.3 Cascade Model
- 3.4 Workload
- 3.5 Power Management Strategies
- 3.6 Power-Performance Metrics
- Power Consumption.
- Response Time.
- Power State Utilisation.
- 3.7 Visualisation
- 4 Results
- 4.1 Case Study: Computational Cluster
- 4.2 Model Validation
- 5 Conclusions and Future Work
- References
- Simulation Techniques
- Rare Event Simulation with Fully Automated Importance Splitting
- 1 Introduction
- 2 Formal Models and Properties
- 3 Stopping Criteria
- 4 Rare Event Simulation Through Importance Splitting
- 5 Fully Automated Importance Splitting
- 6 Experimental Validation
- 7 Concluding Remarks
- References
- Speed-Up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction
- 1 Introduction
- 2 A Brief Introduction of PCTMC
- 3 Directed Relation Graph with Error Propagation
- 3.1 Group-Based Direct Coupling Coefficient
- 3.2 Indirect Coupling Coefficient
- 4 Statistical Model Reduction
- 4.1 Statistical Transition Rate Evaluation
- 4.2 Model Reduction Algorithms
- Algorithm with Fixed Length Sampling.
- Algorithm with Flexible Length Sampling.
- 4.3 Comparison of the Two Algorithms
- 5 Modelling Language and Model Definition
- 5.1 PALOMA
- 5.2 Model Definition
- 6 Experiments
- 6.1 Discussion
- 7 Conclusion
- References
- Author Index
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