Despite their wide deployment, distributed systems remain notoriously hard to reason about. Unexpected interleavings of concurrent operations and failures may lead to undefined behaviors and cause serious consequences. We present Morpheus, the first concurrency testing tool leveraging partial order sampling, a randomized testing method formally analyzed and empirically validated to provide strong probabilistic guarantees of error-detection, for real-world distributed systems. Morpheus introduces conflict analysis to further improve randomized testing by predicting and focusing on operations that affect the testing result. Inspired by the recent shift in building distributed systems using higher-level languages and frameworks, Morpheus targets Erlang. Evaluation on four popular distributed systems in Erlang including RabbitMQ, a message broker service, and Mnesia, a distributed database in the Erlang standard libraries, shows that Morpheus is effective: It found previously unknown errors in every system checked, 11 total, all of which are flaws in their core protocols that may cause deadlocks, unexpected crashes, or inconsistent states.

We present POS, a concurrency testing approach that samples the partial order of concurrent programs. POS uses a novel priority-based scheduling algorithm that dynamically reassigns priorities regarding the partial order information and formally ensures that each partial order will be explored with significant probability. POS is simple to implement and provides a probabilistic guarantee of error detection better than state-of-the-art sampling approaches. Evaluations show that POS is effective in covering the partial-order space of micro-benchmarks and finding concurrency bugs in real-world programs, such as Firefox’s JavaScript engine SpiderMonkey. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Web applications are getting ubiquitous every day because they offer many useful services to consumers and businesses. Many of these web applications are quite storage-intensive. Cloud computing offers attractive and economical choices for meeting their storage needs. Unfortunately, it remains challenging for developers to best leverage them to minimize cost. This paper presents Grandet, an extensible storage system that significantly reduces storage cost for web applications deployed in the cloud. Grandet provides both a key-value interface and a file system interface, supporting a broad spectrum of web applications. Under the hood, it supports multiple heterogeneous stores and unifies them by placing each data object at the store deemed most economical. We implemented Grandet on Amazon Web Services and evaluated Grandet on a diverse set of four popular open-source web applications. Our results show that Grandet reduces their cost by an average of 42.4%, and it is fast, scalable, and easy to use. The source code of Grandet is at http://columbia.github.io/grandet.

Among all classes of parallel programming abstractions, lock-free data structures are considered one of the most scalable and efficient thanks to their fine-grained style of synchronization. However, they are also challenging for developers and tools to verify because of the huge number of possible interleavings that result from fine-grained synchronizations.

Mobile apps bring unprecedented levels of convenience, yet they are often buggy, and their bugs offset the convenience the apps bring. A key reason for buggy apps is that they must handle a vast variety of system and user actions such as being randomly killed by the OS to save resources, but app developers, facing tough competitions, lack time to thoroughly test these actions. AppDoctor is a system for efficiently and effectively testing apps against many system and user actions, and helping developers diagnose the resultant bug reports. It quickly screens for potential bugs using approximate execution, which runs much faster than real execution and exposes bugs but may cause false positives. From the reports, AppDoctor automatically verifies most bugs and prunes most false positives, greatly saving manual inspection effort. It uses action slicing to further speed up bug diagnosis. We implement AppDoctor in Android. It operates as a cloud of physical devices or emulators to scale up testing. Evaluation on 53 out of 100 most popular apps in Google Play and 11 of the most popular open-source apps shows that, AppDoctor effectively detects 72 bugs---including two bugs in the Android framework that affect all apps---with quick checking sessions, speeds up testing by 13.3 times, and vastly reduces diagnosis effort.