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Traditionally, network monitoring and analytics systems rely on aggregation (e.g., flow records) or sampling to cope with high packet rates. This has the downside that, in doing so, we lose data granularity and accu- racy, and, in general, limit the possible network analytics we can perform. Recent proposals leveraging software- defined networking or programmable hardware provide more fine-grained, per-packet monitoring but are still based on the fundamental principle of data reduction in the network, before analytics. In this paper, we pro- vide a first step towards a cloud-scale, packet-level mon- itoring and analytics system based on stream processing entirely in software. Software provides virtually unlim- ited programmability and makes modern ( e.g.,machine-learning) network analytics applications possible. We identify unique features of network analytics applica- tions which enable the specialization of stream process- ing systems. As a result, an evaluation with our pre- liminary implementation shows that we can scale up to several million packets per second per core and together with load balancing and further optimizations, the vision of cloud-scale per-packet network analytics is possible. 

We revisit the gap between what distributed systems need from the transport layer and what protocols in wide deployment provide. Such a gap complicates the implementation of distributed systems and impacts their performance. We introduce Tunable Multicast Communication (TMC), an abstraction that allows developers to easily specialize communication channels in distributed systems. TMC is presented as a deployable and extensible user-space library that exposes high-level tunable guarantees. TMC has the potential of improving the performance of distributed applications with minimal-to-zero development and deployment effort.