Packet header overhead is a persistent source of inefficiency in packet-switched networks, reducing goodput and increasing network load. Trends like tunneling further increase this overhead, significantly impacting flow completion times. While, in principle, it is possible to compress these headers, existing methods require specialized hardware on every hop to compress/decompress the packet to/from custom header formats.

Memory disaggregation allows applications running on compute servers to expand their pool of available memory capacity by leveraging remote resources through low-latency networks. Unfortunately, in existing software-level disaggregation frameworks, the simple act of issuing requests to remote memory---paid on every access---can consume many CPU cycles. This overhead represents a direct cost to disaggregation, not only on the throughput of remote memory access but also on application logic, which must contend with the framework's CPU overheads.

When diagnosing performance anomalies, it is often useful to reason about why a packet experienced the queuing that it did. To that end, we observe that queuing is both a result of historical effects and the current state of the network. Further, both factors involve short and long timescales by nature. Existing work fails to provide insight that satisfies all of these needs.

For public networks like the Internet and those of many clouds, end-host applications can use any congestion control protocol they wish. This protocol diversity and application autonomy are only increasing over time. While in-network support for fairness is an attractive solution for reigning in the inequity, existing solutions still have difficulty scaling to today's networks using today's devices. In this paper, we present Cebinae, a mechanism for augmenting existing networks of legacy hosts with penalties for flows that exceed their max-min fair share. Cebinae is compatible with all of the congestion control protocols in today's Internet, is deployable on commodity programmable switches, and scales orders of magnitude beyond existing alternatives.

For modern data center switches, the ability to---with minimum latency and maximum flexibility--- react to current network conditions is important for managing increasingly dynamic networks. The traditional approach to implementing this type of behavior is through a control plane that is orders of magnitude slower than the speed at which typical data center congestion events occur. More recent alternatives like programmable switches can remember statistics about passing traffic and adjust behavior accordingly, but unfortunately, their capabilities severely limit what can be done.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made substantial progress, but CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables communication from ZigBee to WiFi. ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). Moreover, we extend ZigFi to multiple-to-one concurrent transmissions. We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state of the arts.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art.