In CacheDirector [EuroSys 2019], we show how we unlocked a performance-enhancing feature that existed in Intel® processors for almost a decade. Our results show that placing incoming packets on the so-called slice of the fast cache memory that is closest to the CPU core that will process the packet could reduce the tail latencies in latency-critical Network Function Virtualization (NFV) service chains by 21.5%. In our USENIX ATC 2020 paper, we demonstrate that optimizing DDIO (a form of direct cache access) could reduce the latency of I/O intensive network functions running at 100Gbps by up to ~30%.
Our technique for load-balancing within a single machine called RSS++ [CoNEXT 2019] incurs up to 14x lower 95th percentile tail latency and orders of magnitude fewer packet drops compared to RSS under high CPU utilization. Our Cheetah [NSDI 2020] work is a load balancer that supports uniform load distribution and guaranteed per-connection-consistency (PCC) while being scalable, memory efficient, resilient to clogging attacks, and fast at processing packets.
Our Metron journal article [ACM TOCS 2021] expands upon the preparatory work for this project (published at NSDI 2018) by making it possible to accommodate black boxes.
PacketMill [ASPLOS 2021] grinds the whole packet processing stack and increases throughput (up to 36.4Gbps – 70%) & reduces latency (up to 101µs – 28%) and enables nontrivial packet processing (e.g. router) at ≈100Gbps , when new packets arrive > 10 × faster than main memory access times, while using only one processing core.
In our FAJITA paper [CoNEXT 2024] we have shown that a commodity server running a chain of stateful network functions can process more than 170 M packets per second (equivalent of 1.4 Tbps if average-sized payloads are stored in a disaggregated fashion as in our earlier Ribosome work [NSDI 2023]). Something else that is interesting and perhaps unexpected is that, unless the number of so-called “elephant flows” is very small, spreading incoming packets among the cores using plain Receive Side Scaling (RSS) outperforms existing approaches that perform fine-grained flow accounting and load-balancing.
Our Packet Order Matters [NSDI 2022] paper (that won the Community Award) shows a surprising result: deliberately delaying packets can improve the performance of backend servers by up to about a factor of 2 (e.g. those used for Network Function Virtualization)! This applies to both throughput and latency (including the time spent in our Reframer).
In our SoCC 2018 paper, we present Kurma, our fast and accurate load balancer for geo-distributed storage systems and demonstrate Kurma’s ability to effectively share load among datacenters while reducing SLO violations by up to a factor of 3 in high load settings or reducing the cost of running the service by up to 17%.
We have also performed additional work on low-latency distributed file systems for the next generation of durable storage. In joint work with researchers spread all over our planet with Waleed Reda as the main author we built the Assise distributed file system that colocates computation with persistent memory modules (PMM) storage. Our evaluation shows that Assise improves write latency up to 22x, throughput up to 56x, fail-over time up to 103x, and scales up to 6x better than its counterparts. Our follow-on LineFS paper [SOSP 2021] (that won a Best Paper Award) offloads CPU-intensive tasks to a SmartNIC (BlueField-1 in our case) for about 80% performance improvement across the board. Our RedN [NSDI 2022] paper shows a surprising result, namely that Remote Direct Memory Access (RDMA), as implemented in widely deployed RDMA Network Interface Cards, is Turing Complete. We leverage this finding to reduce the tail latency of services running on busy servers by 35x!
