EECS Faculty Answer Your Questions
About the Future of Technology

Location: 465 Soda
Time: 2:00 - 4:00 PM

The mission of the RAD Lab (Reliable Adaptive Distributed systems) is to create the technology to enable a single person to create and operate the next great Internet service. That is, to create a service like Ebay without having to build a company the size of Ebay. Leaders in machine learning, networking, and systems have formed interdisciplinary teams to fulfill this mission. If successful, we hope to enable a Fortune 1 million of Internet entrepreneurs.

The RAD Lab involves 7 faculty, 30 graduate students, and a few staff. To increase interdisciplinary interactions, we remodeled the south end of the 4th floor of Soda Hall to create an open collaborative environment. The new space overshot this target, and as we believe it is now accelerating our research.

Our funding comes primarily from industry and state matching programs, with our foundation partners being Google, Microsoft, and Sun Microsystems and with our affiliate members being Cisco Systems, Fujitsu, HP, IBM, NetApp, Oracle, Siemens, and VMware.

Please come join us in our new lab in 465 Soda for a poster session where we will share with you our work in:

• Exposing network service failures with datapath tracing (George Porter)
• Instrumenting Hadoop with XTrace (Matei Zaharia, Andy Konwinski)
• Bridging the Gap: X-Trace & Ruby on Rails (Lisa Fowler)
• Virtual Machine-aware Malware (Erika Chin, David Zhu)
• Using Structured Random Data to Precisely Fuzz Media Players (Cynthia Sturton, Barret Rhoden)
• Owning Your Inb0x: Attacks on Spam Filtering (Blaine Nelson, Charles Sutton)
• Workload Generation and Use of Synthetic Web Apps for Benchmarking in the Datacenter (Henry Cook)
• Hand Gesture Recognition Using Orientation Histograms (Kristal Sauer)
• Power Aware Workload Balancing (Junda Liu)
• Reducing Power Consumption in Network Switches (Ganesh Ananthanarayanan)
• Higher-Performance, Nonrelational Database for Scalable Rails Apps (Michael Armbrust, Barret Rhoden, David Zhu)
• Response-Time Modeling for Power-Aware Resource Allocation (Peter Bodik, Charles Sutton)
• A Prototype of the Instrumentation Backplane (Ari Rabkin, Stephen Dawson-Haggerty)
• Using Machine Learning to Predict Performance of a Parallel Database System (Archana Ganapathi)
• Deploying a Prototype Policy-Aware Switching Layer (Dilip A. Joseph)
• A Machine-Learning-Enabled Router to Deal with Local-Area Congestion (Kurtis Heimerl)
• Deterministic Replay of Distributed Applications (Gautam Altekar)
• Power Saving vs. Disk Failure Rates in the DETER Cluster (David Patterson, Anthony Joseph)
• Understanding the Performance of Web 2.0 Applications in Ruby on Rails (Alex Bain, Arthur Klepchukov, Will Sobel, Armando Fox, UC Berkeley; Shanti Subramanyam, Akara Sucharitakul, Sun Microsystems)
• RubyOnRails.berkeley.edu (improving infrastructure, meetups, student opportunities...) (Armando Fox, Will Sobel)
• D3: Declarative Distributed Debugging (Byung-Gon Chun, Kuang Chen, Gunho Lee)
• D2aiquiri: Diagnosing Performance Problems From Trace Data Using  Probabilistic Models (Charles Sutton, George Porter)