DataSys: Data-Intensive Distributed Systems LaboratoryData-Intensive Distributed Systems Laboratory

Illinois Institute of Technology
Department of Computer Science

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2nd Greater Chicago Area System Research Workshop (GCASR) 2013

Northwestern University, Evanston IL -- May 3rd, 2013

Workshop Program

We are pleased to announce that the final program will be composed of 1 keynote presentation, 15 oral presentations, and 39 poster presentations (separated into session 1 and session 2). We have 57 workshop presenters, representing 10 different institutions (ANL, DePaul, Google, IIT, Northwestern, Notre Dame, Purdue, UChicago, UIC, and UIUC). The workshop will start at 8AM with breakfast, and will end at 6:15PM.

Keynote Presentation

Oral Presentations

Time Description
8:00 - 8:30 Breakfast
8:30 - 8:40 Opening Remarks
Nikos Hardavellas, Northwestern
Ioan Raicu, IIT
8:40 - 9:25 Keynote
Pete Beckman, ANL
  Session 1: Architecture, Embedded and Reconfigurable Systems
9:25 - 9:50 The 10x10 Project: Can we have both extreme energy efficiency and programmability?
Andrew A. Chien, UChicago
9:50 - 10:15 Collectively Achieve Fairest Distribution of Discrete Resources with Limited Sharing: Complexity and Protocols
Wenjing Rao, UIC
10:15 - 10:30 Break
  Session 2: HPC, Cloud, and Warehouse-scale Computing
10:30 - 10:55 Warehouse-scale Computing: Challenges and Opportunities
Nicholas Kidd, Google
10:55 - 11:20 Scaling Up Without Blowing Up
Douglas Thain, Notre Dame
11:20 - 11:45 The Case for Limping-Hardware Tolerant Clouds
Haryadi Gunawi, UChicago
11:45 - 12:10 Toward Smart HPC via Active Learning and Intelligent Scheduling
Zhiling Lan, IIT
12:10 - 12:55 Lunch
12:55 - 1:40 Poster Session 1
Distributed Systems and Storage
  Session 3: Networking and Storage
1:40 - 2:05 Pushing experiments to the Internet's edge
Fabian E. Bustamante, Northwestern
2:05 - 2:30 Increasing Network Resiliency by Optimally Assigning Diverse Variants to Routing Nodes
Cristina Nita-Rotaru, Purdue
2:30 - 2:55 Parallel Networks and Storage for Predictable End-to-End Data Movement
Eun-Sung Jung, ANL
2:55 - 3:20 System Design Considerations of Big Data Processing
Xian-He Sun, IIT
3:20 - 4:05 Break
Poster Session 2
Architecture, Data-bases, Networking, OS, Programming Languages, and Security
  Session 4: Operating Systems and Security
4:05 - 4:30 Recent Research in the V3VEE Project
Peter Dinda, Northwestern
4:30 - 4:55 Ethos: A Layered Approach to Secure Applications
Jon A Solworth, UIC
4:55 - 5:20 DroidChameleon: Evaluating Android Anti-malware against Transformation Attacks
Yan Chen, Northwestern
  Session 5: Programming Languages and Database Systems
5:20 - 5:45 Diderot: a Domain-Specific Language for Portably Parallel Scientific Visualization and Image Analysis
Gordon Kindlmann, UChicago
5:45 - 6:10 The (Potential) Perks of Integrating Provenance Support into Database Engines
Boris Glavic, IIT
6:10 Closing Remarks
Nikos Hardavellas, Northwestern
Ioan Raicu, IIT

Poster Session 1

Distributed Systems and Storage

Title Name Institution
A Decoupled Execution Paradigm for Data-Intensive High-End Computing Yanlong Yin IIT
Distributed Kev-Value Store on Cloud Tonglin Li IIT
Enabling and Optimizing Parallel File Write in HDFS Xi Yang IIT
FusionFS: a distributed file system for large scale data-intensive computing Dongfang Zhao IIT
HPC Analytics for Extreme Scale Computing Li Yu IIT
MATRIX: MAny-Task computing execution fabRIc at eXascales Anupam Rajendran IIT
Measuring Power Consumption on IBM Blue Gene/Q Sean Wallace IIT
NoVoHT: a Lightweight Dynamic Persistent NoSQL Key/Value Store *Kevin Brandstatter IIT
Power-Aware Job Scheduling on Production HPC Systems Zhou Zhou IIT
Toward Petascale Cosmology Simulations Jingjin Wu IIT
Towards a Provenance-aware Distributed Filesystem Chen Shou IIT
Towards Distributed Batch Scheduling Xiaobing Zhou IIT
Understanding the cost of the cloud for scientific applications Iman Sadooghi IIT
Community sensing under (soft) control John Rula Northwestern
Blockus: Efficient Out-of-Core Computation Using Blocks Erik Bodzsar UChicago
Exploiting Global View for Resilience (GVR) Hajime Fujita UChicago
The Global View Resilience Model Zachary Rubenstein UChicago
Linking and Loading Scientific Workflows Casey Robinson Notre Dame
Scaling Work Queue for the Cloud with Hierarchy Michael Albrecht Notre Dame
Software Engineering Challenges and Tools in Distributed Computing Workspace for Civil Engineering Applications Peter Sempolinski Notre Dame

Poster Session 2

Architecture, Data-bases, Networking, OS, Programming Languages, and Security

Title Name Institution
Mastering chaos with cost-effective sampling Mona Rahimi DePaul
Accelerating Scientific Workflow Applications with GPUs *Dustin Shahidehpour IIT
Enabling Dynamic Memory Management Support for MTC on NVIDIA GPUs *Ben Grimmer IIT
GeMTC: GPU enabled Many-Task Computing Scott J. Krieder IIT
Understanding the Costs of Many-Task Computing Workloads
on Intel Xeon Phi Coprocessors
*Jeffrey Johnson IIT
Characterizing Broadband Services with Dasu Zachary Bischof Northwestern
Elastic Fidelity: Trading-off Computational Accuracy for Energy Reduction Georgios Tziantzioulis Northwestern
Experiments at the Internet’s Edge with Dasu Mario A. Sanchez Northwestern
Galaxy: Pushing the Power and Bandwidth Walls
with Optically Connected Disintegrated Processors
Yigit Demir Northwestern
Shifting GEARS to Enable Guest-context Virtual Services Kyle Hale Northwestern
Understanding Timing Errors Patterns Yuanbo Fan Northwestern
VMM-based Emulation of Intel Hardware Transactional Memory Maciej Swiech Northwestern
Compiler optimization for massively parallel data flow Timothy Armstrong UChicago
The 10x10 Project: Can we have both extreme energy efficiency and programmability? Raihan ur Rasool UChicago
When is Multi-version Checkpointing Needed? Guoming Lu UChicago
MinimaLT: Minimal Latency Networking Through Better Security Xu Zhang UIC
Scalable Authentication Infrastructure Wenyuan Fei UIC
Simple and Secure Networking Yaohua Li UIC
Automatic Memory Deduplication Support in Hypervisor Fangzhou Yao UIUC

Note that * denotes undergraduate students.