Data-intensive batch jobs increasingly compete for resources with customer-facing online workloads in modern data centers. Today, the two classes of workloads run on separate infrastructures using different resource managers that pursue different ...

We present Grappa, a modern take on software distributed shared memory (DSM) for in-memory data-intensive applications. Grappa enables users to program a cluster as if it were a single, large, non-uniform memory access (NUMA) machine. Performance scales ...

Most recent storage devices, such as NAND flash-based solid state drives (SSDs), provide low access latency and high degree of parallelism. However, conventional file systems, which are designed for slow hard disk drives, often encounter severe ...

Applications implement their own update protocols to ensure consistency of data on the file system. However, since current file systems provide only a preliminary ordering guarantee, notably fsync, these update protocols become complex, slow, and error-...

Peripherals fail. Yet, modern embedded systems largely leave the burden of tolerating peripheral failures to the programmer. This paper presents Phoenix, a semi-automated peripheral recovery system for resource-constrained embedded systems. Phoenix ...

Cloud storage systems typically use three-way random replication to guard against data loss within the cluster, and utilize cluster geo-replication to protect against correlated failures. This paper presents a much lower cost alternative to full cluster ...