Using Static Analysis to Diagnose
Misconfigured Open Source Systems Software

Ariel Rabkin, UC Berkeley

1:00pm Monday 5 March 2012, ITE 325b UMBC

Ten years ago, few software developers worked on distributed systems. Today, developers often run code on clusters, relying on large open-source software stacks to manage resources. These systems are challenging to configure and debug. Fortunately, developments in program analysis have given us new tools for managing the complexity of modern software. This talk will show how static analysis can help users configure their systems. I present a technique that builds an explicit table mapping a program's possible error messages to the options that might cause them. As a result, users can get immediate feedback on how to resolve configuration errors.

Ari Rabkin is a PhD student in Computer Science at UC Berkeley working in the AMP lab. His current research interest is the software engineering and administration challenges of big-data systems. He is particularly interested in applying program analysis techniques to tasks like log analysis and configuration debugging. His broader interests focus on systems and security, including improving system usability by making systems easier to understand, the connections between computer science research and technology policy, developing program analysis techniques that work acceptably well on large, complex, messy software systems.

Host: Anupam Joshi
See http://csee.umbc.edu/talks for more information

Self-sustainable Cyber-physical System Design

Dr. Nilanjan Banerjee
University of Arkansas Fayetteville

1:00pm Tuesday 13 March 2012, ITE 325b UMBC

Renewable energy can enable diverse self-sustainable cyber-physical systems with applications ranging from healthcare to off-grid home energy management. However, there are several challenges that need to be addressed before such systems can be realized. For instance, how do we balance the small and often variable energy budgets imposed by renewables with system functionality? How can we design sensitive physical sensors and efficient harvesting circuits for mW energy sources such as sound and indoor light? For systems such as off-grid homes that interact with humans, how do we balance demand and supply while being cognizant to usability needs?

In this talk, I will present techniques that address these challenges. Specifically, I will propose a Hierarchical Power Management paradigm that combines platforms with varied energy needs to balance energy consumption and functionality, the design of an efficient harvester for sound scavenging, and sensitive ECG sensors. I will also present a measurement study that reveals the energy management challenges faced by off-grid home residents. Finally, I will conclude with the design of a solar replayer platform that allows immense flexibility in evaluating solar panel driven systems, and works for a wide range of panels.

Nilanjan Banerjee is an Assistant Professor in the department of Computer Science and Computer Engineering at University of Arkansas Fayetteville. He graduated with a M.S. and a Ph.D. from the University of Massachusetts at Amherst in 2009 and a BTech. (Hons.) from IIT Kharagpur in 2004. He has won the Yahoo! Outstanding dissertation award at UMass, a best undergraduate thesis award at IIT Kharagpur, and an Outstanding Researcher award at University of Arkansas. He is a 2011 NSF Career awardee and has won three other NSF awards (including the NSF I-Corp grant). His research interests span renewable energy driven systems, healthcare systems, and mobile systems.

Host: Anupam Joshi
See http://csee.umbc.edu/talks for more information

Correlation Aware Optimizations for Analytic Databases

Hideaki Kimura, Brown University

1:00pm Friday 9 March 2012, ITE 325b, UMBC

Recent years have seen that the analysis of large data-sets is crucially important in a wide range of business, governmental, and scientific applications. For example, research projects in astronomy need to analyze petabytes of image data taken from telescopes. Providing a fast and scalable analytical data management system for such users has become increasingly important.

The major bottlenecks for analytics on such big data are disk- and network-I/O. Because the data is too large to fit in RAM, each query causes substantial disk I/O. Traditional database systems provide indexes to speed up disk reads, but many analytic queries do not benefit from indexes because data is scattered over a large number of disk blocks and disk seeks are prohibitively expensive. Furthermore, such huge data sets need to be partitioned and distributed over hundreds or many thousands of nodes. When a query requires more than one data at once, such as a query involving a JOIN operation, the data management system must transmit a large amount of data over the network. For example, the Shuffle phase in Map-Reduce systems copies file blocks over the network and causes a significant bottleneck in many cases.

Our approach to tackling these challenges in big data analytics is to exploit correlations. I will describe our correlation-aware indexing, replication, and data placement which make big data analytics faster and more scalable.

Finally, if time allows, I will also introduce another on-going project to develop a scalable transactional processing system on modern hardware in collaboration with Hewlett-Packard Laboratories.

Hideaki Kimura is a doctoral candidate in the Computer Science Department at Brown University. His main research interests are in data management systems. His dissertation research with Prof. Stan Zdonik is on correlation-based optimizations for large analytic databases. He also worked on transaction processing systems exploiting modern hardware at HP Labs.

Host: Anupam Joshi
See http://csee.umbc.edu/talks for more information

Analytics for Detecting Web and Social Media Abuse

Dr. Justin Ma, UC Berkeley

1:00pm Friday 16 March 2012, ITE 325, UMBC

The Web and online social media provide invaluable communication services to a global Internet user base. The tremendous success of these services, however, has also created valuable opportunities for criminals and other miscreants to abuse them for their own gain. As a result, it is both an important yet challenging problem to detect, monitor, and curtail this abuse. However, the large scale and diversity of these services, combined with the tactics used by attackers, make it difficult to discern one clear and robust signal for detecting abuse. One approach, relying on domain expertise, is to construct a small set of well-crafted heuristics, but such heuristics tend to rapidly become obsolete. In this talk, I will describe more robust approaches based on machine learning, statistical modeling, and large-scale analytics of large data sets.

First I will describe online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. This application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the features that typify malicious URLs evolve continuously. Motivated by this application, we built a real-time system to gather URL features and analyze them against a source of labeled URLs from a large Web mail provider. Our system adapts in an online fashion to the evolving characteristics of malicious URLs, achieving daily classification accuracies up to 99% over a balanced data set.

Next I will describe our ongoing efforts for creating analytics for detecting social media abuse. Deciding on a universal definition of social media abuse is difficult, as abuse is often in the eye of the beholder. In light of this challenge, we explore a more formal definition based on information theory. In particular, we hypothesize that messages with low information content are likely to be abusive. From this, we develop a measure of content complexity to identify abusive users that shows promise in our early evaluations.

In addition to our own experiments in the lab, this work has found success in practice as well. Companies serving hundreds of millions of users have adopted these ideas to improve abuse detection within their own services.

Justin Ma is a postdoc in the UC Berkeley AMPLab. His primary research is in systems security, and his other interests include applications of machine learning to systems problems, systems for large-scale machine learning, and the impact of energy availability on computing. He received B.S. degrees in Computer Science and Mathematics from the University of Maryland in 2004, and he received his Ph.D. in Computer Science from UC San Diego in 2010.

Host: Anupam Joshi
See http://csee.umbc.edu/talks for more information

Program Goals:

• Provide top freshmen and sophomore students with exciting and interactive courses in Android development to build development competencies in the Android platform, culminating in application development at the end of the program.
• Allow students to get an inside look into the culture and work of Google.
• Facilitate connections between students and Googlers that will allow students to discover career paths and create meaningful academic experiences.

The Institute for Genome Sciences (IGS) at the University of Maryland School of Medicine is a globally recognized center of scientific research, led by Dr. Claire M. Fraser-Liggett. IGS scientists apply the powerful tools of genomics and bioinformatics to biomedical and environmental challenges, and collaborate with world-renowned researchers in immunology, virology, infectious disease, and epidemiology.

Our internship program provides opportunities for students at all levels to participate in IGS research. Interns are matched to a faculty or senior staff member who is working on projects most suited to the intern's area of interest.

If you are interested in being part of the cutting edge research at IGS, please fill out and submit the internship application form. We will review your areas of interest and skills and contact you if we are able to make a match with one of our available opportunities. Please bear in mind that we have more applicants who wish to work with us than we have available internship positions so only a subset of applications will result in placement.

Visit http://www.igs.umaryland.edu/careers/internships.php for more information.

Google is running an Android Camp this summer where 30 selected students will attend an all-expense-paid program at Google’s headquarters in Mountain View, California, July 22-28.

The camp will include an interactive and collaborative curriculum focusing on a practical introduction to developing applications for Android and will explore the concepts behind Android, the framework for constructing an application, and the tools for developing, testing, and publishing software for the platform. Students will also get the opportunity to enjoy technical talks by Googlers, and social activities around the Bay Area.

To be eligible, you must be a current freshman or sophomore majoring in Computer Science, Computer Engineering or related area and have at least a year of proven Java development experience.  The application deadline is March 11.

For more information and to apply, see the 2012 Google Android Camp site.

Building and Testing Distributed Systems

Dr. Charles Killian
Purdue University, Computer Science

1:00pm Friday, 2 March 2012, ITE325 UMBC

Building distributed systems is particularly difficult because of the asynchronous, heterogeneous, and failure-prone environment where these systems must run. This asynchrony makes verifying the correctness of systems implementations even more challenging. Tools for building distributed systems must often strike a compromise between reducing programmer effort and increasing system efficiency. In my research, we strive to introduce a limited amount of structure and limitations to implementations to enable a wide range of analysis and development assistance. Most prominently, we have built the Mace language and runtime, which translates a concise, expressive distributed system specification into a C++ implementation. The Mace specification importantly exposes three key pieces of structure: atomic events, explicit state, and explicit messaging.

With a few additional contextual annotations, we show how we can support intra-node parallel event processing of these atomic events while still preserving sequenal event consistency—even using variably available computing resources distributed across a cluster. By leveraging these three structural elements, we have further built tools such as a model checker capable of detecting liveness violations in systems code, a performance tester, and an automated malicious protocol tester. Recent research has also explored applications of these key structures in legacy software, that has produced a log anaysis tool that can detect performance problems, and a malicious fault injector that can discover successful performance attacks. Mace has been in development since 2004 and has been used to build a wide variety of Internet-ready distributed systems both by myself and by researchers at places such as Cornell University, Microsoft Research (Redmond, Silicon Valley, and Beijing), HP Labs, UCLA, EPFL, and UCSD. This talk will give an overview of my research, presenting the execution model and its checker, support for event parallelization, and our more recent testing tools.

Charles Killian is an Assistant Professor in the Department of Computer Science at Purdue University. He received an NSF CAREER award in 2011, as well as an HP Open Innovation award. In 2008 he completed his Ph.D. in Computer Science from the University of California, San Diego under the supervision of Amin Vahdat. Before transferring to UCSD in August 2004, he completed his Masters in Computer Science from Duke University with Amin Vahdat. His systems and networking research focuses on building and testing distributed systems, and bridges this research with software engineering, security, data mining, and programming languages. Since 2004 he has implemented the Mace programming language and runtime, built numerous distributed systems, and designed MaceMC, the first model checker capable of finding liveness violations in unmodified systems code and 2007 best paper award at NSDI. Chip has built many additional tools and enhancements since then, including performance testing, work on parallel event processing, automated attack discovery, and data mining logs to discover performance problems.