Muhammad Ali Gulzar

Publications

2021

1. [SOCC 2021] OptDebug: Fault-Inducing Operation Isolation for Dataflow Applications

   Muhammad Ali Gulzar, and Miryung Kim

   In The 12th ACM Symposium on Cloud Computing 2021

   13 Pages. 30% Acceptance Rate

   Abstract

   Fault-isolation is extremely challenging in large scale data processing in cloud environments. Data provenance (DP) is a dominant existing approach to isolate data records responsible for a given output. However, as the name suggests, data provenance concerns fault isolation only in the data-space, as opposed to fault isolation in the code-space–how can we precisely localize operations or APIs responsible for a given suspicious or incorrect result? We present OptDebug that identifies fault-inducing operations in a dataflow application using three insights. First, debugging is easier with a small-scale input than a large-scale input. So it uses data provenance to simplify the original set of input records data to a smaller set leading to test failures and test successes. Second, keeping track of operation provenance is crucial for debugging. Thus, it leverages automated taint analysis to propagate the lineage of operations downstream with individual records. Lastly, each operation may contribute to test failures to a different degree. Thus OptDebug ranks each operation’s spectra–the relative frequency in failing vs. passing tests. In our experiments, OptDebug achieves 100% recall and 86% precision in terms of detecting faulty operations and reduces the debugging time by 17X compared to a naïve approach. Overall, OptDebug shows great promise in improving developer productivity in today’s complex data processing pipelines by obviating the need to re-execute the program repetitively with different inputs and manually examine program traces to isolate buggy code.

2. [IMC 2021] TrackerSift: Untangling Mixed Tracking and Functional Web Resources

   Abdul Hadi Amjad, Muhammad Saleem, Muhammad Ali Gulzar*, Zubair Shafiq*, and Fareed Zaffar*

   In Proceedings of the 2021 ACM Internet Measurement Conference 2021

   8 Pages. 27.9% Acceptance Rate

   Abstract

   Trackers typically circumvent filter lists used by privacy-enhancing content blocking tools by changing the domains or URLs of their resources. Filter list maintainers painstakingly attempt to keep up in the ensuing arms race by frequently updating the filter lists. Trackers have recently started to mix tracking and functional resources, putting content blockers in a bind: risk breaking legitimate functionality if they act and risk missing privacy-invasive advertising and tracking if they do not. In this paper, we conduct a large-scale measurement study of such mixed (i.e., both tracking and functional) resources on 100K websites. We propose TrackerSift, an approach that progressively classifies and untangles mixed web resources at multiple granularities of analysis (domain, hostname, script, and method). Using TrackerSift, we find that 83% of the domains can be separated as tracking or functional, and the remaining 17% (11.8K) domains are classified as mixed. For the mixed domains, 52% of the hostnames can be separated, and the remaining 48% (12.3K) hostnames are classified as mixed. For the mixed hostnames, 94% of the JavaScript snippets can be separated, and the remaining 6% (21.1K) scripts are classified as mixed. For the mixed scripts, 91% of the JavaScript methods can be separated, and the remaining 9% (5.5K) methods are classified as mixed. Overall, TrackerSift is able to attribute 98% of all requests to tracking or functional resources at the finest level of granularity. Our analysis shows that mixed resources at different granularities are typically served from CDN and other general-purpose content hosting domains and hostnames or as inlined and bundled scripts. Our results highlight the opportunities for fine-grained content blocking to remove mixed resources without breaking legitimate functionality.

3. [HiPS 2021] Towards a Serverless Bioinformatics Cyberinfrastructure Pipeline

   Shunyu David Yao, Muhammad Ali Gulzar, Liqing Zhang, and Ali R. Butt

   In Proceedings of the 1st Workshop on High Performance Serverless Computing 2021

   8 Pages. Workshop Paper.

   Abstract

   Function-as-a-Service (FaaS) and the serverless computing model offer a powerful abstraction for supporting large-scale applications in the cloud. A major hurdle in this context is that it is non-trivial to transform an application, even an already containerized one, to a FaaS implementation. In this paper, we take the first step towards supporting easier and efficient application transformation to FaaS. We present a systematic scheme to transform applications written in Python into a set of functions that can then be automatically deployed atop platforms such as AWS Lamda. We target a Bioinformatics cyberinfrastructure pipeline, CIWARS, that provides waste-water analysis for the identification of antibiotic-resistant bacteria and viruses such as SARS-CoV-2. Based on our experience with enabling FaaS-based CIWARS, we develop a methodology that would help the conversion of other similar applications to the FaaS model. Our evaluation shows that our approach can correctly transform CIWARS to FaaS, and the new FaaS-based CIWARS incurs only negligible(≤2%) less than 2% overhead for representative workloads.

2020

1. [SOCC 2020] Influence-Based Provenance for Dataflow Applications with Taint Propagation

   Jason Teoh, Muhammad Ali Gulzar, and Miryung Kim

   In The 11th ACM Symposium on Cloud Computing 2020

   12 Pages. Full Paper. 24.4% Acceptance Rate

   Abstract

   Debugging big data analytics often requires a root cause analysis to pinpoint the precise culprit records in an input dataset responsible for incorrect or anomalous output. Existing debugging or data provenance approaches do not track fine-grained control and data flows in user-defined application code; thus, the returned culprit data is often too large for manual inspection and expensive post-mortem analysis is required.We design FlowDebug to identify a highly precise set of input records based on two key insights. First, FlowDebug precisely tracks control and data flow within user-defined functions to propagate taints at a fine-grained level by inserting custom data abstractions through automated source to source transformation. Second, it introduces a novel notion of influence-based provenance for many-to-one dependencies to prioritize which input records are more responsible than others by analyzing the semantics of a user-defined function used for aggregation. By design, our approach does not require any modification to the framework’s runtime and can be applied to existing applications easily. FlowDebug significantly improves the precision of debugging results by up to 99.9 percentage points and avoids repetitive re-runs required for post-mortem analysis by a factor of 33 while incurring an instrumentation overhead of 0.4X - 6.1X on vanilla Spark.

2. [ASE 2020] BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction

   Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar, Rohan Padhye, and Miryung Kim

   In The 35th IEEE/ACM International Conference on Automated Software Engineering 2020

   12 Pages. Full Paper. 22.5% Acceptance Rate

   Abstract

   As big data analytics become increasingly popular, data-intensive scalable computing (DISC) systems help address the scalability issue of handling large data. However, automated testing for such data-centric applications is challenging, because data is often incomplete, continuously evolving, and hard to know a priori. Fuzz testing has been proven to be highly effective in other domains such as security; however, it is nontrivial to apply such traditional fuzzing to big data analytics directly for three reasons: (1) the long latency of DISC systems prohibits the applicability of fuzzing: naı̈ve fuzzing would spend 98% of the time in setting up a test environment; (2) conventional branch coverage is unlikely to scale to DISC applications because most binary code comes from the framework implementation such as Apache Spark; and (3) random bit or byte level mutations can hardly generate meaningful data, which fails to reveal real-world application bugs.We propose a novel coverage-guided fuzz testing tool for big data analytics, called BigFuzz. The key essence of our approach is that: (a) we focus on exercising application logic as opposed to increasing framework code coverage by abstracting the DISC framework using specifications. BigFuzz performs automated source to source transformations to construct an equivalent DISC application suitable for fast test generation, and (b) we design schema-aware data mutation operators based on our in-depth study of DISC application error types. BigFuzz speeds up the fuzzing time by 78 to 1477X compared to random fuzzing, improves application code coverage by 20% to 271%, and achieves 33% to 157% improvement in detecting application errors. When compared to the state of the art that uses symbolic execution to test big data analytics, BigFuzz is applicable to twice more programs and can find 81% more bugs.

3. [ESEC/FSE 2020] Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?

   Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, and Miryung Kim

   In The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2020

   12 Pages. Full Paper. 28.0% Acceptance Rate

   Abstract

   Recent effort to test deep learning systems has produced an intuitive and compelling test criterion called neuron coverage (NC), which resembles the notion of traditional code coverage. NC measures the proportion of neurons activated in a neural network and it is implicitly assumed that increasing NC improves the quality of a test suite. In an attempt to automatically generate a test suite that increases NC, we design a novel diversity promoting regularizer that can be plugged into existing adversarial attack algorithms. We then assess whether such attempts to increase NC could generate a test suite that (1) detects adversarial attacks successfully, (2) produces natural inputs, and (3) is unbiased to particular class predictions. Contrary to expectation, our extensive evaluation finds that increasing NC actually makes it harder to generate an effective test suite: higher neuron coverage leads to fewer defects detected, less natural inputs, and more biased prediction preferences. Our results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem.

4. [ICSE 2020] HeteroRefactor: Refactoring for Heterogeneous Computing with FPGA

   Jason Lau*, Aishwarya Sivaraman*, Qian Zhang*, Muhammad Ali Gulzar, Jason Cong, and Miryung Kim

   In 2020 IEEE/ACM 42nd International Conference on Software Engineering 2020

   13 Pages. Full Paper. 20.9% Acceptance Rate

   Abstract

   Heterogeneous computing with field-programmable gate-arrays (FPGAs) has demonstrated orders of magnitude improvement in computing efficiency for many applications. However, the use of such platforms so far is limited to a small subset of programmers with specialized hardware knowledge. High-level synthesis (HLS) tools made significant progress in raising the level of programming abstraction from hardware programming languages to C/C++, but they usually cannot compile and generate accelerators for kernel programs with pointers, memory management, and recursion, and require manual refactoring to make them HLS-compatible. Besides, experts also need to provide heavily handcrafted optimizations to improve resource efficiency, which affects the maximum operating frequency, parallelization, and power efficiency.We propose a new dynamic invariant analysis and automated refactoring technique, called HeteroRefactor. First, HeteroRefactor monitors FPGA-specific dynamic invariants—the required bitwidth of integer and floating-point variables, and the size of recursive data structures and stacks. Second, using this knowledge of dynamic invariants, it refactors the kernel to make traditionally HLS-incompatible programs synthesizable and to optimize the accelerator’s resource usage and frequency further. Third, to guarantee correctness, it selectively offloads the computation from CPU to FPGA, only if an input falls within the dynamic invariant. On average, for a recursive program of size 175 LOC, an expert FPGA programmer would need to write 185 more LOC to implement an HLS compatible version, while HeteroRefactor automates such transformation. Our results on Xilinx FPGA show that HeteroRefactor minimizes BRAM by 83% and increases frequency by 42% for recursive programs; reduces BRAM by 41% through integer bitwidth reduction; and reduces DSP by 50% through floating-point precision tuning.

5. [ICSE Demo 2020] BigTest: Symbolic Execution Based Systematic Test Generation Tool for Apache Spark

   Muhammad Ali Gulzar, Madan Musuvathi, and Miryung Kim

   In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings 2020

   4 Pages. Demonstration Paper. 33.3% Acceptance Rate

   Abstract

   Data-intensive scalable computing (DISC) systems such as Google’s MapReduce, Apache Hadoop, and Apache Spark are prevalent in many production services. Despite their popularity, the quality of DISC applications suffers due to a lack of exhaustive and automated testing. Current practices of testing DISC applications are limited to using a small random sample of the entire input dataset which merely exposes any program faults. Unlike SQL queries, testing DISC applications has new challenges due to a composition of both dataflow and relational operators, and user-defined functions (UDF) that could be arbitrarily long and complex.To address this problem, we demonstrate a new white-box testing framework called BigTest that takes an Apache Spark program as input and automatically generates synthetic, concrete data for effective and efficient testing. BigTest combines the symbolic execution of UDFs with the logical specifications of dataflow and relational operators to explore all paths in a DISC application. Our experiments show that BigTest is capable of generating test data that can reveal up to 2X more faults than the entire data set with 194X less testing time. We implement BigTest in a Java-based command line tool with a pre-compile binary jar. It exposes a configuration file in which a user can edit preferences, including the path of a target program, the upper bound of loop exploration, and a choice of theorem solver. The demonstration video of BigTest is available at https://youtu.be/OeHhoKiDYso and BigTest is available at https://github.com/maligulzar/BigTest.

2019

1. [ESEC/FSE 2019] White-box Testing of Big Data Analytics with Complex User-defined Functions

   Muhammad Ali Gulzar, Shaghayegh Mardani, Madanlal Musuvathi, and Miryung Kim

   In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2019

   12 Pages. Full Paper. 24.4% Acceptance Rate

   Abstract

   Data-intensive scalable computing (DISC) systems such as Google’s MapReduce, Apache Hadoop, and Apache Spark are being leveraged to process massive quantities of data in the cloud. Modern DISC applications pose new challenges in exhaustive, automatic testing because they consist of dataflow operators, and complex user-defined functions (UDF) are prevalent unlike SQL queries. We design a new white-box testing approach, called BigTest to reason about the internal semantics of UDFs in tandem with the equivalence classes created by each dataflow and relational operator. Our evaluation shows that, despite ultra-large scale input data size, real world DISC applications are often significantly skewed and inadequate in terms of test coverage, leaving 34% of Joint Dataflow and UDF (JDU) paths untested. BigTest shows the potential to minimize data size for local testing by 10^5 to 10^8 orders of magnitude while revealing 2X more manually-injected faults than the previous approach. Our experiment shows that only few of the data records (order of tens) are actually required to achieve the same JDU coverage as the entire production data. The reduction in test data also provides CPU time saving of 194X on average, demonstrating that interactive and fast local testing is feasible for big data analytics, obviating the need to test applications on huge production data.

2. [SoCC 2019] PerfDebug: Performance Debugging of Computation Skew in Dataflow Systems

   Jason Teoh, Muhammad Ali Gulzar, Harry Xu, and Miryung Kim

   In Proceedings of the 2019 Symposium on Cloud Computing 2019

   12 Pages. Full Paper. 24.8% Acceptance Rate

   Abstract

   Performance is a key factor for big data applications, and much research has been devoted to optimizing these applications. While prior work can diagnose and correct data skew, the problem of computation skew—abnormally high computation costs for a small subset of input data—has been largely overlooked. Computation skew commonly occurs in real-world applications and yet no tool is available for developers to pinpoint underlying causes.To enable a user to debug applications that exhibit computation skew, we develop a post-mortem performance debugging tool. PerfDebug automatically finds input records responsible for such abnormalities in a big data application by reasoning about deviations in performance metrics such as job execution time, garbage collection time, and serialization time. The key to PerfDebug’s success is a data provenance-based technique that computes and propagates record-level computation latency to keep track of abnormally expensive records throughout the pipeline. Finally, the input records that have the largest latency contributions are presented to the user for bug fixing. We evaluate PerfDebug via in-depth case studies and observe that remediation such as removing the single most expensive record or simple code rewrite can achieve up to 16X performance improvement.

3. [ICSE SEIP 2019] Perception and Practices of Differential Testing

   Muhammad Ali Gulzar, Yongkang Zhu, and Xiaofeng Han

   In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice 2019

   10 Pages. Full Paper. 22.2% Acceptance Rate

   Abstract

   Tens of thousands engineers are contributing to Google’s codebase that spans billions of lines of code. To ensure high code quality, tremendous amount of effort has been made with new testing techniques and frameworks. However, with increasingly complex data structures and software systems, traditional test case based testing strategies cannot scale well to achieve the desired level of test adequacy. Differential (Diff) testing is one of the new testing techniques adapted to fill this gap. It uses the same input to run two versions of a software system, namely base and test, where base is the verified/tested version of the system while test is the modified version. The output of two runs are then thoroughly compared to find abnormalities that may lead to possible bugs.Over the past few years, differential testing has been quickly adopted by hundreds of teams across all major product areas at Google. Meanwhile, many new differential testing frameworks were developed to simplify the creation, maintenance, and analysis of diff tests. Curious by this emerging popularity, we conducted the first empirical study on differential testing in practice at large scale. In this study, we investigated common practices and usage of diff tests. We further explore the features of diff tests that users value the most and the pain points of using diff tests. Through this user study, we discovered that differential testing does not replace fine-grained testing techniques such as unit tests. Instead it supplements existing testing suites. It helps users verify the impact on unmodified and unfamiliar components in the absence of a test oracle. In terms of limitations, diff tests often take long time to run and appear to generate noisy and flaky outcomes. Finally, we highlight problems (including smart data differencing, sampling, and traceability) to guide future research in differential testing.

2018

1. [ICDCS 2018] LogLens: A Real-Time Log Analysis System

   Biplob Debnath, Mohiuddin Solaimani, Muhammad Ali Gulzar, Nipon Arora, Cristian Lumezanu, Jianwu Xu, Bo Zong, Hui Zhang, Guofei Jiang, and Latifur Khan

   In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) 2018

   11 Pages. Full Paper. 20.6% Acceptance Rate
2. [VLDB Journal 2018] Adding Data Provenance Support to Apache Spark

   Matteo Interlandi, Ari Ekmekji, Kshitij Shah, Muhammad Ali Gulzar, Sai Deep Tetali, Miryung Kim, Todd Millstein, and Tyson Condie

   The VLDB Journal 2018

   21 Pages. VLDB Journal Paper.

   Abstract

   Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours collecting evidence (e.g., from log files) and performing trial-and-error debugging. To aid this effort, we built Titian, a library that enables data provenance–tracking data through transformations–in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds–orders of magnitude faster than alternative solutions–while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

3. [ESEC/FSE Demo 2018] BigSift: Automated Debugging of Big Data Analytics in Data-intensive Scalable Computing

   Muhammad Ali Gulzar, Siman Wang, and Miryung Kim

   In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2018

   4 Pages. Demonstration Paper. 38.8% Acceptance Rate

   Abstract

   Developing Big Data Analytics often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions made about data. When errors (e.g. program crash, outlier results, etc.) arise, developers are often interested in pinpointing the root cause of errors. To address this problem, BigSift takes an Apache Spark program, a user-defined test oracle function, and a dataset as input and outputs a minimum set of input records that reproduces the same test failure by combining the insights from delta debugging with data provenance. The technical contribution of BigSift is the design of systems optimizations that bring automated debugging closer to a reality for data intensive scalable computing. BigSift exposes an interactive web interface where a user can monitor a big data analytics job running remotely on the cloud, write a user-defined test oracle function, and then trigger the automated debugging process. BigSift also provides a set of predefined test oracle functions, which can be used for explaining common types of anomalies in big data analytics–for example, finding the origin of the output value that is more than k standard deviations away from the median. The demonstration video is available at https://youtu.be/jdBsCd61a1Q.

4. [ICSE ACM Student Research Competition 2018] Interactive and Automated Debugging for Big Data Analytics ( ACM Student Research Competition Gold Medal Winner)

   Muhammad Ali Gulzar,

   In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings 2018

   3 Pages. Short Paper.

   Abstract

   An abundance of data in many disciplines of science, engineering, national security, health care, and business has led to the emerging field of Big Data Analytics that run in a cloud computing environment. To process massive quantities of data in the cloud, developers leverage Data-Intensive Scalable Computing (DISC) systems such as Google’s MapReduce, Hadoop, and Spark.Currently, developers do not have easy means to debug DISC applications. The use of cloud computing makes application development feel more like batch jobs and the nature of debugging is therefore post-mortem. Developers of big data applications write code that implements a data processing pipeline and test it on their local workstation with a small sample data, downloaded from a TB-scale data warehouse. They cross fingers and hope that the program works in the expensive production cloud. When a job fails or they get a suspicious result, data scientists spend hours guessing at the source of the error, digging through post-mortem logs. In such cases, the data scientists may want to pinpoint the root cause of errors by investigating a subset of corresponding input records.The vision of my work is to provide interactive, real-time and automated debugging services for big data processing programs in modern DISC systems with minimum performance impact. My work investigates the following research questions in the context of big data analytics: (1) What are the necessary debugging primitives for interactive big data processing? (2) What scalable fault localization algorithms are needed to help the user to localize and characterize the root causes of errors? (3) How can we improve testing efficiency during iterative development of DISC applications by reasoning the semantics of dataflow operators and user-defined functions used inside dataflow operators in tandem?To answer these questions, we synthesize and innovate ideas from software engineering, big data systems, and program analysis, and coordinate innovations across the software stack from the user-facing API all the way down to the systems infrastructure.

2017

1. [SoCC 2017] Automated Debugging in Data-intensive Scalable Computing

   Muhammad Ali Gulzar, Matteo Interlandi, Xueyuan Han, Mingda Li, Tyson Condie, and Miryung Kim

   In Proceedings of the 2017 Symposium on Cloud Computing 2017

   15 Pages. Full Paper. 23.6% Acceptance Rate

   Abstract

   Developing Big Data Analytics workloads often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions made about data. When errors (e.g., program crash, outlier results, etc.) arise, developers are often interested in identifying a subset of the input data that is able to reproduce the problem. BigSift is a new faulty data localization approach that combines insights from automated fault isolation in software engineering and data provenance in database systems to find a minimum set of failure-inducing inputs. BigSift redefines data provenance for the purpose of debugging using a test oracle function and implements several unique optimizations, specifically geared towards the iterative nature of automated debugging workloads. BigSift improves the accuracy of fault localizability by several orders-of-magnitude (∼103 to 107\texttimes) compared to Titian data provenance, and improves performance by up to 66\texttimes compared to Delta Debugging, an automated fault-isolation technique. For each faulty output, BigSift is able to localize fault-inducing data within 62% of the original job running time.

2. [SIGMOD Demo 2017] Debugging Big Data Analytics in Spark with BigDebug

   Muhammad Ali Gulzar, Matteo Interlandi, Tyson Condie, and Miryung Kim

   In Proceedings of the 2017 ACM International Conference on Management of Data 2017

   4 Pages. Demonstration Paper. 34% Acceptance Rate

   Abstract

   To process massive quantities of data, developers leverage Data-Intensive Scalable Computing (DISC) systems such as Apache Spark. In terms of debugging, DISC systems support only post-mortem log analysis and do not provide any debugging functionality. This demonstration paper showcases BigDebug: a tool enhancing Apache Spark with a set of interactive debugging features that can help users in debug their Big Data Applications.

2016

1. [ICSE 2016] BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark

   Muhammad Ali Gulzar, Matteo Interlandi, Seunghyun Yoo, Sai Tetali, Tyson Condie, Todd Millstein, and Miryung Kim

   In 2016 IEEE/ACM 38th International Conference on Software Engineering 2016

   12 Pages. Full Paper. 19.1% Acceptance Rate

   Abstract

   Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today’s datacenters is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires rethinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user.First, BigDebug’s simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BigDebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BigDebug supports debugging at interactive speeds with minimal performance impact.

2. [SoCC 2016] Optimizing Interactive Development of Data-Intensive Applications

   Matteo Interlandi, Sai Deep Tetali, Muhammad Ali Gulzar, Joseph Noor, Tyson Condie, Miryung Kim, and Todd Millstein

   In Proceedings of the Seventh ACM Symposium on Cloud Computing 2016

   13 Pages. Full Paper. 25.1% Acceptance Rate

   Abstract

   Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch jobs that execute programs (e.g., queries) compiled from a high-level language. These programs are often developed interactively by posing ad-hoc queries over the base data until a desired result is generated. We observe that there can be significant overlap in the structure of these queries used to derive the final program. Yet, each successive execution of a slightly modified query is performed anew, which can significantly increase the development cycle. Vega is an Apache Spark framework that we have implemented for optimizing a series of similar Spark programs, likely originating from a development or exploratory data analysis session. Spark developers (e.g., data scientists) can leverage Vega to significantly reduce the amount of time it takes to re-execute a modified Spark program, reducing the overall time to market for their Big Data applications.

3. [VLDB 2016] Titian: Data Provenance Support in Spark ( The "Best of VLDB" Paper)

   Matteo Interlandi, Kshitij Shah, Sai Deep Tetali, Muhammad Ali Gulzar, Seunghyun Yoo, Miryung Kim, Todd Millstein, and Tyson Condie

   Proc. VLDB Endow. 2016

   12 Pages. Full Paper. 21.2% Acceptance Rate

   Abstract

   Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders-of-magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

4. [HotCloud 2016] Interactive Debugging for Big Data Analytics

   Muhammad Ali Gulzar, Xueyuan Han, Matteo Interlandi, Shaghayegh Mardani, Sai Deep Tetali, Todd Millstein, and Miryung Kim

   In 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16) 2016

   7 Pages. Workshop Paper. 30.8% Acceptance Rate

   Abstract

   An abundance of data in many disciplines has accelerated the adoption of distributed technologies such as Hadoop and Spark, which provide simple programming semantics and an active ecosystem. However, the current cloud computing model lacks the kinds of expressive and interactive debugging features found in traditional desktop computing. We seek to address these challenges with the development of BIGDEBUG, a framework providing interactive debugging primitives and tool-assisted fault localization services for big data analytics. We showcase the data provenance and optimized incremental computation features to effectively and efficiently support interactive debugging, and investigate new research directions on how to automatically pinpoint and repair the root cause of errors in large-scale distributed data processing.

5. [ESEC/FSE Demo 2016] BigDebug: Interactive Debugger for Big Data Analytics in Apache Spark

   Muhammad Ali Gulzar, Matteo Interlandi, Tyson Condie, and Miryung Kim

   In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering 2016

   5 Pages. Demonstration Paper. 40.1% Acceptance Rate

   Abstract

   To process massive quantities of data, developers leverage data-intensive scalable computing (DISC) systems in the cloud, such as Google’s MapReduce, Apache Hadoop, and Apache Spark. In terms of debugging, DISC systems support post-mortem log analysis but do not provide interactive debugging features in realtime. This tool demonstration paper showcases a set of concrete usecases on how BigDebug can help debug Big Data Applications by providing interactive, realtime debug primitives. To emulate interactive step-wise debugging without reducing throughput, BigDebug provides simulated breakpoints to enable a user to inspect a program without actually pausing the entire computation. To minimize unnecessary communication and data transfer, BigDebug provides on-demand watchpoints that enable a user to retrieve intermediate data using a guard and transfer the selected data on demand. To support systematic and efficient trial-and-error debugging, BigDebug also enables users to change program logic in response to an error at runtime and replay the execution from that step. BigDebug is available for download at http://web.cs.ucla.edu/ miryung/software.html

2015

1. [PACIS 2015] A Classification Based Framework to Predict Viral Threads

   Hashim Sharif, Saad Ismail, Shehroze Farooqi, Mohammad Taha Khan, Muhammad Ali Gulzar, Hasnain Lakhani, Fareed Zaffar, and Ahmed Abbasi

   In The Pacific Asia Conference on Information Systems (PACIS) 2015

   13 Pages. Full Paper.