Massive storage systems composed of tens of thou-sands of disks are increasingly common in high-performance computing data centers. With such an enormous number of components integrated within the storage system the probability for correlated failures across a large number of components becomes a critical concern in preventing data loss. In this paper we reconsider the efficiency of traditional declustered parity data protection schemes in the presence of correlated failures. To better protect against correlated failures we introduce Single-Overlap Declustered Parity (SODP), a novel declustered parity design that tolerates more disk failures than traditional declus-tered parity. We then introduce CoFaCTOR, a tool for exploring operational reliability in the presence of many types of correlated failures. By seeding CoFaCTOR with real failure traces from LANL's data center we are able to create a failure model that accurately describes the existing file system's failure model and can use that model to generate failure data for hypothetical system designs. Our evaluation using CoFaCTOR traces shows that when compared to the state of the art our SODP-based placement algorithms can achieve a 30x improvement in the probability of data loss during failure bursts and achieves similar data protection using only half as much parity overhead.

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.

We reveal loopholes of Speculative Execution (SE) implementations under a unique fault model: node-level network throughput degradation. This problem appears in many data-parallel frameworks such as Hadoop MapReduce and Spark. To address this, we present PBSE, a robust, path-based speculative execution that employs three key ingredients: path progress, path diversity, and path-straggler detection and speculation. We show how PBSE is superior to other approaches such as cloning and aggressive speculation under the aforementioned fault model. PBSE is a general solution, applicable to many data-parallel frameworks such as Hadoop/HDFS+QFS, Spark and Flume.

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).

The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the network traffic generated in the shuffle phase, which plays a critical role in performance enhancement. Traditionally, a hash function is used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size associated with each key are not taken into consideration. In this paper, we study to reduce network traffic cost for a MapReduce job by designing a novel intermediate data partition scheme. Furthermore, we jointly consider the aggregator placement problem, where each aggregator can reduce merged traffic from multiple map tasks. A decomposition-based distributed algorithm is proposed to deal with the large-scale optimization problem for big data application and an online algorithm is also designed to adjust data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost under both offline and online cases.