LEADER 11910nam 22008415 450 001 9910483293603321 005 20200706015208.0 010 $a3-319-23267-3 024 7 $a10.1007/978-3-319-23267-6 035 $a(CKB)3890000000001381 035 $a(SSID)ssj0001558467 035 $a(PQKBManifestationID)16182673 035 $a(PQKBTitleCode)TC0001558467 035 $a(PQKBWorkID)14819448 035 $a(PQKB)10657416 035 $a(DE-He213)978-3-319-23267-6 035 $a(MiAaPQ)EBC6307270 035 $a(MiAaPQ)EBC5610599 035 $a(Au-PeEL)EBL5610599 035 $a(OCoLC)919254517 035 $a(PPN)188460853 035 $a(EXLCZ)993890000000001381 100 $a20150821d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aComputer Performance Engineering $e12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings /$fedited by Marta Beltrán, William Knottenbelt, Jeremy Bradley 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (X, 307 p. 102 illus.) 225 1 $aProgramming and Software Engineering ;$v9272 300 $aIncludes index. 311 $a3-319-23266-5 327 $aIntro -- 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. 327 $a4.1 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. 327 $a2.4 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. 327 $a6 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. 327 $a4 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. 330 $aThis book constitutes the refereed proceedings of the 12th European Workshop on Computer Performance Engineering, EPEW 2015, held in Madrid, Spain, in August/September 2015. The 19 papers presented in this volume were carefully reviewed and selected from 39 submissions. They were organized in topical sections named: applications; modelling techniques, software performance, and simulation techniques. 410 0$aProgramming and Software Engineering ;$v9272 606 $aComputer system failures 606 $aMathematical statistics 606 $aComputer logic 606 $aSoftware engineering 606 $aComputer simulation 606 $aSystem Performance and Evaluation$3https://scigraph.springernature.com/ontologies/product-market-codes/I13049 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X 606 $aSoftware Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/I14029 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 615 0$aComputer system failures. 615 0$aMathematical statistics. 615 0$aComputer logic. 615 0$aSoftware engineering. 615 0$aComputer simulation. 615 14$aSystem Performance and Evaluation. 615 24$aProbability and Statistics in Computer Science. 615 24$aLogics and Meanings of Programs. 615 24$aSoftware Engineering. 615 24$aSimulation and Modeling. 676 $a004 702 $aBeltrán$b Marta$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKnottenbelt$b William$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBradley$b Jeremy$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483293603321 996 $aComputer Performance Engineering$93004631 997 $aUNINA