Just In Time Delivery: Leveraging Operating Systems Knowledge for Better Datacenter Congestion Control - Ousterhout, Amy; Belay, Adam; Zhang, Irene (Workshop on Hot Topics in Cloud Computing (HotCloud), 2019)
Network links and server CPUs are heavily contended resources in modern datacenters. To keep tail latencies low, datacenter operators drastically overprovision both types of resources today, and there has been significant research into effectively managing network traffic and CPU load. However, all of this work looks at the two resources in isolation. In this paper, we make the observation that, in the datacenter, the allocation of network and CPU resources should be co-designed for the most efficiency and the best response times. For example, while congestion control protocols can prioritize traffic from certain flows, it is wasted work if the traffic arrives at an overloaded server that will only enqueue the request. Likewise, allocating more CPU resources to an application will not improve its performance if network congestion is causing a bottleneck in its requests. This paper explores the potential benefits of such a co-designed resource allocator and considers the recent work in both CPU scheduling and congestion control that is best suited to such a system. We propose a Chimera, a new datacenter OS that integrates a receiver-based congestion control protocol with OS insight into application queues, using the recent Shenango operating system.