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ICPE'16 Proceedings Table of Contents ICPE
2016 – General Chairs' Welcome ICPE
2016 – Program Chairs' Welcome |
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Case
Studies from the Real World: The Importance of Measurement and Analysis
in Building Better Systems (Page
1) |
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Maximum
Likelihood Estimation of Closed Queueing Network Demands from Queue
Length Data (Page
3) |
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A
Cost/Benefit Approach to Performance Analysis (Page
15) Automated
Extraction of Network Traffic Models Suitable for Performance Simulation (Page
27) Learning
from Source Code History to Identify Performance Failures (Page
37) Resource
and Performance Distribution Prediction for Large Scale Analytics Queries (Page
49) |
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Automatic
Performance Modelling from Application Performance Management (APM)
Data: An Experience Report (Page
55) Analyzing
the Efficiency of a Green University Data Center (Page
63) Interconnect
Emulator for Aiding Performance Analysis of Distributed Memory Applications (Page
75) Parallel
Graph Processing: Prejudice and State of the Art (Page
85) |
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| (Return to Top) | Work-In-Progress and Vision Papers Asking
"What?", Automating the "How?": The Vision of Declarative Performance
Engineering (Page
91) Predicting
the System Performance by Combining Calibrated Performance Models of
its Components: A Preliminary Study (Page
95) Towards
Using Code Coverage Metrics for Performance Comparison on the Implementation
Level (Page 101) Performance
Extrapolation of Io Intensive Workloads: Work in Progress (Page
105) BFT-Bench:
A Framework to Evaluate BFT Protocols (Page
109) Towards
Performance and Scalability Analysis of Distributed Memory Programs
on Large-Scale Clusters (Page
113) |
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Empirical
Analysis of Performance Problems at Code Level (Page
117) Sustaining
Runtime Performance while Incrementally Modernizing Transactional Monolithic
Software Towards Microservices (Page
121) |
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PROST:
Predicting Resource Usages with Spatial and Temporal Dependencies (Page
125) SPEC
Research Group's Cloud Working Group: RG Cloud Group (Page
127) Integrating
Faban with Docker for Performance Benchmarking (Page
129) Which
Cloud Auto-Scaler Should I Use for My Application? Benchmarking Auto-Scaling
Algorithms (Page
131) |
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Microservices
for Scalability: Keynote Talk Abstract (Page
133) |
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Performance
Modeling of Maximal Sharing (Page
135) |
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Variations
in CPU Power Consumption (Page
147) End-to-End
Java Security Performance Enhancements for Oracle SPARC Servers (Page
159) Accelerating
the Optimal Trade-Off Circular Harmonic Function Filter Design on Multicore
Systems (Page 167) |
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A
Resource Contention Analysis Framework for Diagnosis of Application
Performance Anomalies in Consolidated Cloud Environments (Page
173) Beyond
Energy-Efficiency: Evaluating Green Datacenter Applications for Energy-Agility (Page
185) Tackling
Latency via Replication in Distributed Systems (Page
197) Enhancing
Rules for Cloud Resource Provisioning via Learned Software Performance
Models (Page 209) |
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Optimized
eeebond: Energy Efficiency with non-Proportional Router Network Interfaces (Page
215) |
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| (Return to Top) | Characterization and Profiling Communication
Characterization and Optimization of Applications Using Topology-Aware
Task Mapping on Large Supercomputers (Page
225) Automatically
Detecting "Excessive Dynamic Memory Allocations Software Performance
Anti-Pattern" (Page
237) Efficient
and Viable Handling of Large Object Traces (Page
249) |
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Cloudy,
Foggy and Misty Internet of Things (Page
261) |
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Efficient
Tracing and Versatile Analysis of Lock Contention in Java Applications
on the Virtual Machine Level (Page
263) Analysis
of Overhead in Dynamic Java Performance Monitoring (Page
275) The
Value of Variance (Page
287) Experimental
Performance Evaluation of Different Data Models for a Reflection Software
Architecture over NoSQl Persistence Layers (Page
297) |
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Optimizing
the Performance-Related Configurations of Object-Relational Mapping
Frameworks Using a Multi-Objective Genetic Algorithm (Page
309) |
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Building
Custom, Efficient, and Accurate Memory Monitoring Tools for Java Applications (Page
321) Automated
Parameterization of Performance Models from Measurements (Page
325) Incorporating
Software Performance Engineering Methods and Practices into the Software
Development Life Cycle (Page
327) |
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