Ariel Rabkin

A view of cloud computing

Clearing the clouds away from the true potential and obstacles posed by this computing capability.

Personal knowledge questions for fallback authentication: security questions in the era of Facebook

Security questions (or challenge questions) are commonly used to authenticate users who have lost their passwords. We examined the password retrieval mechanisms for a number of personal banking websites, and found that many of them rely in part on security questions with serious usability and security weaknesses. We discuss patterns in the security questions we observed. We argue that todays personal security questions owe their strength to the hardness of an information-retrieval problem. However, as personal information becomes ubiquitously available online, the hardness of this problem, and security provided by such questions, will likely diminish over time. We supplement our survey of bank security questions with a small user study that supplies some context for how such questions are used in practice.

Static extraction of program configuration options

Many programs use a key-value model for configuration options. We examined how this model is used in seven open source Java projects totaling over a million lines of code. We present a static analysis that extracts a list of configuration options for a program. Our analysis finds 95% of the options read by the programs in our sample, making it more complete than existing documentation. Most configuration options we saw fall into a small number of types. A dozen types cover 90% of options. We present a second analysis that exploits this fact, inferring a type for most options. Together, these analyses enable more visibility into program configuration, helping reduce the burden of configuration documentation and configuration debugging.

Precomputing possible configuration error diagnoses

Complex software packages, particularly systems software, often require substantial customization before being used. Small mistakes in configuration can lead to hard-todiagnose error messages. We demonstrate how to build a map from each program point to the options that might cause an error at that point. This can aid users in troubleshooting these errors without any need to install or use additional tools. Our approach relies on static dataflow analysis, meaning all the analysis is done in advance. We evaluate our work in detail on two substantial systems, Hadoop and the JChord program analysis toolkit, using failure injection and also by using log messages as a source of labeled program points. When logs and stack traces are available, they can be incorporated into the analysis. This reduces the number of false positives by nearly a factor of four for Hadoop, at the cost of approximately one minutes work per unique query

Experiences teaching MapReduce in the cloud

We describe our experiences teaching MapReduce in a large undergraduate lecture course using public cloud services. Using the cloud, every student could carry out scalability benchmarking assignments on realistic hardware, which would have been impossible otherwise. Over two semesters, over 500 students took our course. We believe this is the first large-scale demonstration that it is feasible to use pay-as-you-go billing in the Cloud for a large undergraduate course. Modest instructor effort was sufficient to prevent students from overspending. Average per-pupil expenses in the Cloud were under

Using clouds for MapReduce measurement assignments

45, less than half our available grant funding. Students were excited by the assignment: 90% said they thought it should be retained in future course offerings.

Above the Clouds: A Berkeley View of Cloud Computing

We describe our experiences teaching MapReduce in a large undergraduate lecture course using public cloud services and the standard Hadoop API. Using the standard API, students directly experienced the quality of industrial big-data tools. Using the cloud, every student could carry out scalability benchmarking assignments on realistic hardware, which would have been impossible otherwise. Over two semesters, over 500 students took our course. We believe this is the first large-scale demonstration that it is feasible to use pay-as-you-go billing in the cloud for a large undergraduate course. Modest instructor effort was sufficient to prevent students from overspending. Average per-pupil expenses in the Cloud were under

Chukwa: a system for reliable large-scale log collection

45. Students were excited by the assignment: 90% said they thought it should be retained in future course offerings.