Although Remote Procedure Calls (RPCs) generate bulk of the network traffic in modern disaggregated datacenters, network-level optimizations still focus on low-level metrics at the granularity of packets, flowlets, and flows. The lack of application-level semantics lead to suboptimal performance as there is often a mismatch between network- and application-level metrics. Indeed, my works on coflow relied on a similar observation for distributed data-parallel jobs. In this work, we focus on leveraging application- and user-level semantics of RPCs to improve application- and user-perceived performance. Specifically, we implement quality-of-service (QoS) primitives at the RPC level by extending classic works on service curves and network calculus. We show that it can be achieved using an edge-based solutions in conjunction with traditional QoS classes in legacy/non-programmable switches.

With the increasing popularity of disaggregated storage and microservice architectures, high fan-out and fan-in Remote Procedure Calls (RPCs) now generate most of the traffic in modern datacenters. While the network plays a crucial role in RPC performance, traditional traffic classification categories cannot sufficiently capture their importance due to wide variations in RPC characteristics. As a result, meeting service-level objectives (SLOs), especially for performance-critical (PC) RPCs, remains challenging.

We present Aequitas, a distributed sender-driven admission control scheme that uses commodity Weighted-Fair Queuing (WFQ) to guarantee RPC-level SLOs. Aequitas maps PC RPCs to higher weight queues. In the presence of network overloads, it enforces cluster-wide RPC latency SLOs by limiting the amount of traffic admitted into any given QoS and downgrading the rest. We show analytically and empirically that this simple scheme works well. When the network demand spikes beyond provisioned capacity, Aequitas achieves a latency SLO that is 3.8× lower than the state-of-art congestion control at the 99.9th-percentile and admits up to 2× more PC RPCs meeting SLO when compared with pFabric, Qjump, D3, PDQ, and Homa. Results in a fleet-wide production deployment at a large cloud provider show a 10% latency improvement.

The inception of this project can be traced back to the spring of 2019, when I visited Google to attend a networking summit. Nandita and I had been trying to collaborate on something since 2016(!), and the time seemed right with Yiwen ready to go for an internship. Over the course of couple summers in 2019 and 2020, and many months before and after, Yiwen managed to build a theoretical framework that added QoS support to the classic network calculus framework in collaboration with Gautam. Yiwen also had a lot of support from Xian, PR , and unnamed others to get the simulator and the actual code implemented and deployed in Google datacenters. They implemented a public-facing/shareable version of the simulator as well! According to Amin and Nandita, this is one of the “big” problems, and I’m happy that we managed to pull it off.

This was my first time working with Google. Honestly, I was quite impressed by the rigor of the internal review process (even though the paper still got rejected a couple times). It’s also my first paper with Gautam after 2012! He was great then, and he’s gotten even better in the past decade. Yiwen is also having a great year with a SIGCOMM paper right after his NSDI one.

This year SIGCOMM PC accepted 55 out 281 submissions after a couple years of record-breaking acceptance rates.