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Achieving low remote memory access latency remains the primary challenge in realizing memory disaggregation over Ethernet within the datacenters. We present EDM that attempts to overcome this challenge using two key ideas. First, while existing network protocols for remote memory access over the Ethernet, such as TCP/IP and RDMA, are implemented on top of the Ethernet MAC layer, EDM takes a radical approach by implementing the entire network protocol stack for remote memory access within the Physical layer (PHY) of the Ethernet. This overcomes fundamental latency and bandwidth overheads imposed by the MAC layer, especially for small memory messages. Second, EDM implements a centralized, fast, in-network scheduler for memory traffic within the PHY of the Ethernet switch. Inspired by the classic Parallel Iterative Matching (PIM) algorithm, the scheduler dynamically reserves bandwidth between compute and memory nodes by creating virtual circuits in the PHY, thus eliminating queuing delay and layer 2 packet processing delay at the switch for memory traffic, while maintaining high bandwidth utilization. Our FPGA testbed demonstrates that EDM's network fabric incurs a latency of only ~300 ns for remote memory access in an unloaded network, which is an order of magnitude lower than state-of-the-art Ethernet-based solutions such as RoCEv2 and comparable to emerging PCIe-based solutions such as CXL. Larger-scale network simulations indicate that even at high network loads, EDM's average latency remains within 1.3x its unloaded latency. 

State-intensive network and distributed applications rely heavily on online caching heuristics for high performance. However, there remains a fundamental performance gap between online caching heuristics and the optimal offline caching algorithm due to the lack of visibility into future state access requests in an online setting. Driven by the observation that state access requests in network and distributed applications are often carried in incoming network packets, we present Seer, an online caching solution for networked systems, that exploits the delays experienced by a packet inside a network - most prominently, transmission and queuing delays - to notify in advance of future packet arrivals to the target network nodes (switches/routers/middleboxes/end-hosts) implementing caching. Using this as a building block, Seer presents the design of an online cache manager that leverages visibility into (partial) set of future state access requests to make smarter prefetching and cache eviction decisions. Our evaluations show that Seer achieves up to 65% lower cache miss ratio and up to 78% lower flow completion time compared to LRU for key network applications over realistic workloads. 

Online traffic classification enables critical applications such as network intrusion detection and prevention, providing Quality-of-Service, and real-time IoT analytics. However, with increasing network speeds, it has become extremely challenging to analyze and classify traffic online. In this paper, we present Leo, a system for online traffic classification at multi-terabit line rates. At its core, Leo implements an online machine learning (ML) model for traffic classification, namely the decision tree, in the network switch's data plane. Leo's design is fast (can classify packets at switch's line rate), scalable (can automatically select a resource-efficient design for the class of decision tree models a user wants to support), and runtime programmable (the model can be updated on-the-fly without switch downtime), while achieving high model accuracy. We implement Leo on top of Intel Tofino switches. Our evaluations show that Leo is able to classify traffic at line rate with nominal latency overhead, can scale to model sizes more than twice as large as state-of-the-art data plane ML classification systems, while achieving classification accuracy on-par with an offline traffic classifier.