Length of presentations (including Q&A):

• Keynotes: 55 minutes
  
• Full papers: 25 minutes
• Short papers: 17 minutes
• Work-in-progress/vision papers: 8 minutes

Session 1: Monitoring and Profiling

Wednesday (April 11th), 10:10-10:30
Session Chair: Manoj Nambiar

• Round-Trip Time Anomaly Detection
  
  (industry short)

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Session 2: Runtime Adaptation I

Wednesday (April 11th), 11:00-12:35
Session Chair: Vittorio Cortellessa

• FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds
  
  (research full)
  
  
• 
  Stefano Conoci; Pierangelo Di Sanzo; Bruno Ciciani; Francesco Quaglia
  (research full)
  
  
  
• Optimising Dynamic Binary Modification Across ARM Microarchitectures
  
  (research full)
  
  
• TESS: Automated Performance Evaluation of Self-Healing and Self-Adaptive Distributed Software Systems
  Jason Porter; Daniel Menasce; Hassan Gomaa; Emad Albassam
  (research short)
  
  

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Session 3: High Performance Computing

Wednesday (April 11th), 13:35-15:10
Session Chair: J. Nelson Amaral

• Involving CPUs into Multi-GPU deep learning
  Tung D. Le; Taro Sekiyama; Yasushi Negishi; Haruki Imai; Kiyokuni Kawachiya
  (research full)
  
  
• Measuring Network Latency Variation Impacts to High Performance Computing Application Performance
  Robert Underwood; Jason Anderson; Amy Apon
  (research full)
  
  
• Pattern-based Modeling of Multiresilience Solutions for High-Performance Computing
  Rizwan Ashraf, Saurabh Hukerikar; Christian Engelmann
  (research short)
  
  
• Energy and performance analysis of parallel particle solvers from the ScaFaCoS library
  
  (research short)
  
  
• An Auto-Tuning Framework for a NUMA-Aware Hessenberg Reduction Algorithm
  

  
  (wip/vision)

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Poster/Demo Session

Wednesday (April 11th), 15:10-16:00

Posters

• 
  
  
  
• Challenges in Multicore Performance Predictions
  Markus Frank, Floriment Klinaku; Steffen Becker
  
  
• Criticality-aware Design Space Exploration for Mixed-Criticality Embedded Systems
  Vittoriano Muttillo, Giacomo Valente; Luigi Pomante
  
  
• Application speedup characterization: modeling parallelization overhead and variations of problem size and number of cores
  Victor H. F. Oliveira, Alex F. A. Furtunato, et al.
  
  
• The Alberta Workloads for the SPEC CPU 2017 Benchmark Suite
  Jose Nelson Amaral, Edson Borin, et al.

Demos

• Spark Job Performance Analysis and Prediction Tool
  Rekha Singhal, Chetan Phalak; Praveen Singh
  
  
• On the Simulation of Processors Enhanced for Security in Virtualization
  Swapneel Mhatre, Priya Chandran; Jithin R.

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Session 4: Runtime Adaptation II

Wednesday (April 11th), 16:00-16:25
Session Chair: Alberto Avritzer

• To Adapt or Not to Adapt? Technical Debt and Learning Driven Self-Adaptation for Managing Runtime Performance
  Tao Chen; Rami Bahsoon; Shuo Wang; Xin Yao
  (research short)
  
  
• The Vision of Self-aware Reordering of Security Network Function Chains
  

  
  (wip/vision)

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Session 5: Load Testing and Benchmarking I

Wednesday (April 11th), 16:25-17:40
Session Chair: Alberto Avritzer

• A Declarative Approach for Performance Tests Execution in Continuous Software Development Environments
  Vincenzo Ferme; Cesare Pautasso
  (research full)
  
  
• quiho: Automated Performance Regression Testing Using Fine Granularity Resource Utilization Profiles
  Ivo Jimenez; Noah Watkins; Michael Sevilla; Jay Lofstead; Carlos Maltzahn
  (research full)
  
  
• Towards an Efficient Benchmark Generation Engine for Garbage Collection
  

  Andres Omar Portillo Dominguez
  (wip/vision)
  
  
• ABench: Big Data Architecture Stack Benchmark
  

  Todor Ivanov; Rekha Singhal
  (wip/vision)
  
  
• Towards Scalability Guidelines for Semantic Container Management
  

  Gunnar Brataas; Bernd Neumayr; Christoph Schuetz; Audun Vennesland
  (wip/vision)

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Session 6: Best Paper Candidates I

Thursday (April 12th), 09:55-10:30
Session Chair: Heiko Koziolek

• ODP: An Infrastructure for On-Demand Service Profiling
  John Nicol, Chen Li, Peinan Chen, Tao Feng; Haricharan Ramachandra
  (industry short)
  
  
• Characterizing the Performance of Concurrent Virtualized Network Functions with OVS-DPDK, FD.IO VPP and SR-IOV
  

  Nikolai Pitaev, Matthias Falkner, Aris Leivadeas; Ioannis Lambadaris

  (industry short)

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Session 7: Best Paper Candidates II

Thursday (April 12th), 11:00-12:30
Session Chair: John Murphy

• Characterizing the Microarchitectural Implications of a Convolutional Neural Network (CNN) Execution on GPUs
  Shi Dong; Xiang Gong; Yifan Sun; Trinayan Baruah; David Kaeli
  (research full)
  
  
  
• User-defined Classification and Multi-level Grouping of Objects in Memory Monitoring
  
  (research full)
  
  
• 
  
  (research full)
  
  

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Session 8: Cloud Computing I

Thursday (April 12th), 13:30-15:05
Session Chair: Steffen Becker

• Virtualization Techniques Compared: Performance, Resource, and Power Usage Overheads in Clouds
  Selome Kostentinos Tesfatsion; Cristian Klein; Johan Tordsson
  (research full)
  
  
• Investigating Performance Metrics for Scaling Microservices in CloudIoT-Environments
  
  (industry full)
  
  
• Performance Prediction of Cloud-Based Big Data Applications
  
  (research short)
  
  
• Runtime Performance Management for Cloud Applications with Adaptive Controllers: An Experience Report
  Cornel Barna, Marin Litoiu, Marios Fokaefs, Mark Shtern; Joe Wigglesworth
  (industry short)
  
  
• A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures
  Erwin van Eyk; Alexandru Iosup; Cristina Abad; Johannes Grohmann; Simon Eismann
  (wip/vision)

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Session 9: Enterprise Applications

Thursday (April 12th), 15:35-16:45
Session Chair: Petr T?ma

• Generating Realistic Workload through End-User Organization Categorization using High Level Business Operation Data
  Gururaj Maddodi, Slinger Jansen; Rolf de Jong
  (industry full)
  
  
• One size does not fit all: In-Test Workload Adaptation for Performance Testing of Enterprise Applications
  Andres Omar Portillo Dominguez; Vanessa Ayala-Rivera; Maciej Kaczmarski; John Murphy; Amarendra Darisa
  (research full)
  
  
• Performance Improvement Barriers for SAP Enterprise Applications: An Analysis of Expert Interviews
  Adrian Streitz, Maximilian Barnert, Harald Kienegger; Helmut Krcmar
  (industry short)

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Session 10: Modeling, Prediction, and Optimization

Friday (April 13th), 09:00-10:30

• Joint Data Compression and Caching: Approaching Optimality with Guarantees
  Jian Li; Faheem Zafari; Don Towsley; Kin K. Leung; Ananthram Swami
  (research full)
  
  
• Choice of SubSystems for Performance Model Simplification
  Farhana Islam; Dorina Petriu; Murray Woodside
  (research full)
  
  
• Optimizing energy-performance trade-offs in IoT applications
  Peter Harrison; Naresh Patel
  (research short)
  
  
• Towards Performance Engineering of Model Transformation
  

  Raffaela Groner; Matthias Tichy; Steffen Becker
  (wip/vision)
  
  
• Performance Modelling of Smart Cyber-Physical Systems
  

  Tomas Bures; Vladimir Matena; Raffaela Mirandola; Lorenzo Pagliari; Catia Trubiani
  (wip/vision)

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Session 11: Cloud Computing II

Friday (April 13th), 11:55-12:30
Session Chair: Wilhelm Hasselbring

• Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation
  
  (research short)
  
  
• Evaluating Scalability and Performance of a Security Management Solution in Large Virtualized Environments
  Lishan Yang, Ludmila Cherkasova, Rajeev Badgujar, Jack Blancaflor, Rahul Konde, Jason Mills; Evgenia Smirni
  (industry short)

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Session 12: Load Testing and Benchmarking II

Friday (April 13th), 13:30-14:40
Session Chair: Evgenia Smirni

• Methods for Quantifying Energy Consumption in TPC-H
  Meikel Poess, Da Qi Ren, Tilmann Rabl; Hans-Arno Jacobsen
  (industry full)
  
  
• Using the Raspberry Pi and Docker for Replicable Performance Experiments
  Holger Knoche; Holger Eichelberger
  (industry full)
  
  
• DIBS: A Data Integration Benchmark Suite
  

  Anthony M. Cabrera; Clayton Faber; Kyle Cepeda; Robert Derber; Cooper Epstein; Jason Zheng; Ron Cytron; Roger Chamberlain
  (wip/vision)
  
  
• SPECnet: Predicting SPEC scores using Deep Learning
  

  Dibyendu Das; Prakash Raghavendra; Arun Ramachandran
  (wip/vision)

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