Data serialization is critical for many datacenter applications, but the memory copies required to move application data into packets are costly. Recent zero-copy APIs expose NIC scatter-gather capabilities, raising the possibility of offloading this data movement to the NIC. However, as the memory coordination required for scatter-gather adds bookkeeping overhead, scatter-gather is not always useful. We describe Cornflakes, a hybrid serialization library stack that uses scatter-gather for serialization when it improves performance and falls back to memory copies otherwise. We have implemented Cornflakes within a UDP and TCP networking stack, across Mellanox and Intel NICs. On a Twitter cache trace, Cornflakes achieves 15.4% higher throughput than prior software approaches on a custom key-value store and 8.8% higher throughput than Redis serialization within Redis.

In response to ossification and privacy concerns, post-TCP transport protocols such as QUIC are designed to be "paranoid"---opaque to meddling middleboxes by encrypting and authenticating the header and payload---making it impossible for Performance-Enhancing Proxies (PEPs) to provide the same assistance as before. We propose a research agenda towards an alternate approach to PEPs, creating a sidecar protocol that is loosely-coupled to the unchanged and opaque, underlying transport protocol. The key technical challenge to sidecar protocols is how to usefully refer to the packets of the underlying connection without ossification. We have made progress on this problem by creating a tool we call a quACK (quick ACK), a concise representation of a multiset of numbers that can be used to efficiently decode the randomly-encrypted packet contents a sidecar has received. We implement the quACK and discuss how to achieve several applications with this approach: alternate congestion control, ACK reduction, and PEP-to-PEP retransmission across a lossy subpath.

Organizations face a data discovery problem when their analysts spend more time looking for relevant data than analyzing it. This problem has become commonplace in modern organizations as: i) data is stored across multiple storage systems, from databases to data lakes, to the cloud; ii) data scientists do not operate within the limits of well-defined schemas or a small number of data sources—instead, to answer complex questions they must access data spread across thousands of data sources. To address this problem, we capture relationships between datasets in an enterprise knowledge graph (EKG), which helps users to navigate among disparate sources. The contribution of this paper is AURUM, a system to build, maintain and query the EKG. To build the EKG, we introduce a Two-step process which scales to large datasets and requires only one-pass over the data, avoiding overloading the source systems. To maintain the EKG without re-reading all data every time, we introduce a resource-efficient sampling signature (RESS) method which works by only using a small sample of the data. Finally, to query the EKG, we introduce a collection of composable primitives, thus allowing users to define many different types of discovery queries. We describe our experience using AURUM in three corporate scenarios and do a performance evaluation of each component.