Today's applications pay a performance penalty for mitigations to protect against transient execution attacks such as Meltdown [32] and Spectre [25]. Such a reduction in performance directly translates to higher operating costs and degraded user experience. This paper measures the performance impact of these mitigations across a range of processors from multiple vendors and across several security boundaries to identify trends over successive generations of processors and to attribute how much of the overall slowdown is caused by each individual mitigation.

Machine learning has made it possible to mount powerful attacks through side channels that have traditionally been seen as challenging to exploit. However, due to the black-box nature of machine learning models, these attacks are often difficult to interpret correctly. Models that detect correlations cannot be used to prove causality or understand an attack's various sources of information leakage.

Multicast patterns are common in cloud computing and datacenter settings. Applications and infrastructure tools such as Spark frequently move large objects around, update files replicated to multiple nodes, or push new versions of programs to compute nodes. Some applications use replication directly, for example to increase fault-tolerance or achieve parallelism. Implementations of Paxos, block chains and other libraries often employ a hand-built reliable multicast as a primitive. Yet operating systems continue to be focused on point-to-point communication solutions such as TCP or RDMA, a hardware layer with TCP-like semantics that offers zero copy transfers, but lacks a reliable multi-destination transfer capability. Our system, RDMC (RDMA Multicast), offers reliable multicast functionality constructed from RDMA unicast. We discuss design choices, present a theoretical analysis of RDMC's robustness to delays and slow network links, and report on experiments that evaluate RDMC over Mellanox RDMA.

Cloud computing services often replicate data and may require ways to coordinate distributed actions. Here we present Derecho, a library for such tasks. The API provides interfaces for structuring applications into patterns of subgroups and shards, supports state machine replication within them, and includes mechanisms that assist in restart after failures. Running over 100Gbps RDMA, Derecho can send millions of events per second in each subgroup or shard and throughput peaks at 16GB/s, substantially outperforming prior solutions. Configured to run purely on TCP, Derecho is still substantially faster than comparable widely used, highly-tuned, standard tools. The key insight is that on modern hardware (including non-RDMA networks), data-intensive protocols should be built from non-blocking data-flow components.

The coming generation of Internet-of-Things (IoT) applications will process massive amounts of incoming data while supporting data mining and online learning. In cases with demanding real-time requirements, such systems behave as smart memories: a high-bandwidth service that captures sensor input, processes it using machine-learning tools, replicates and stores "interesting" data (discarding uninteresting content), updates knowledge models, and triggers urgently-needed responses.