We present a fast and scalable testing approach for datacenter/cloud systems such as Cassandra, Hadoop, Spark, and ZooKeeper. The uniqueness of our approach is in its ability to overcome the path/state-space explosion problem in testing workloads with complex interleavings of messages and faults. We introduce three powerful algorithms: state symmetry, event independence, and parallel flips, which collectively makes our approach on average 16x (up to 78x) faster than other state-of-the-art solutions. We have integrated our techniques with 8 popular datacenter systems, successfully reproduced 12 old bugs, and found 10 new bugs --- all were done without random walks or manual checkpoints.

In big data and cloud computing era, reliability of distributed systems is extremely important. Unfortunately, distributed concurrency bugs, referred to as DCbugs, widely exist. They hide in the large state space of distributed cloud systems and manifest non-deterministically depending on the timing of distributed computation and communication. Effective techniques to detect DCbugs are desired. This paper presents a pilot solution, DCatch, in the world of DCbug detection. DCatch predicts DCbugs by analyzing correct execution of distributed systems. To build DCatch, we design a set of happens-before rules that model a wide variety of communication and concurrency mechanisms in real-world distributed cloud systems. We then build runtime tracing and trace analysis tools to effectively identify concurrent conflicting memory accesses in these systems. Finally, we design tools to help prune false positives and trigger DCbugs. We have evaluated DCatch on four representative open-source distributed cloud systems, Cassandra, Hadoop MapReduce, HBase, and ZooKeeper. By monitoring correct execution of seven workloads on these systems, DCatch reports 32 DCbugs, with 20 of them being truly harmful.

We highlight the problem of scalability bugs, a new class of bugs that appear in "cloud-scale" distributed systems. Scalability bugs are latent bugs that are cluster-scale dependent, whose symptoms typically surface in large-scale deployments, but not in small or medium-scale deployments. The standard practice to test large distributed systems is to deploy them on a large number of machines ("real-scale testing"), which is difficult and expensive. New methods are needed to reduce developers' burdens in finding, reproducing, and debugging scalability bugs. We propose "scale check," an approach that helps developers find and replay scalability bugs at real scales, but do so only on one machine and still achieve a high accuracy (i.e., similar observed behaviors as if the nodes are deployed in real-scale testing).

We present TaxDC, the largest and most comprehensive taxonomy of non-deterministic concurrency bugs in distributed systems. We study 104 distributed concurrency (DC) bugs from four widely-deployed cloud-scale datacenter distributed systems, Cassandra, Hadoop MapReduce, HBase and ZooKeeper. We study DC-bug characteristics along several axes of analysis such as the triggering timing condition and input preconditions, error and failure symptoms, and fix strategies, collectively stored as 2,083 classification labels in TaxDC database. We discuss how our study can open up many new research directions in combating DC bugs.

We conduct a comprehensive study of development and deployment issues of six popular and important cloud systems (Hadoop MapReduce, HDFS, HBase, Cassandra, ZooKeeper and Flume). From the bug repositories, we review in total 21,399 submitted issues within a three-year period (2011-2014). Among these issues, we perform a deep analysis of 3655 "vital" issues (i.e., real issues affecting deployments) with a set of detailed classifications. We name the product of our one-year study Cloud Bug Study database (CbsDB) [9], with which we derive numerous interesting insights unique to cloud systems. To the best of our knowledge, our work is the largest bug study for cloud systems to date.