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To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However, bottlenecks caused by the increasing number of concurrent clients, resulting in a large number of concurrent flows, have received little attention. In this work, we focus on identifying such bottlenecks. In particular, we define two broad categories of problems; namely, admitting more packets into the network stack than can be handled efficiently, and increasing per-packet overhead within the stack. We show that these problems contribute to high CPU usage and network performance degradation in terms of aggregate throughput and RTT. Our measurement and analysis are performed in the context of the Linux networking stack, the most widely used publicly available networking stack. Further, we discuss the relevance of our findings to other network stacks. The goal of our work is to highlight considerations required in the design of future networking stacks to enable efficient handling of large numbers of clients and flows 

Modern end-host network stacks have to handle traffic from tens of thousands of flows and hundreds of virtual machines per single host, to keep up with the scale of modern clouds. This can cause congestion for traffic egressing from the end host. The effects of this congestion have received little attention. Currently, an overflowing queue, like a kernel queuing discipline, will drop incoming packets. Packet drops lead to worse network and CPU performance by inflating the time to transmit the packet as well as spending extra effort on retransmissions. In this paper, we show that current end-host mechanisms can lead to high CPU utilization, high tail latency, and low throughput in cases of congestion of egress traffic within the end host. We present zD, a framework for applying backpressure from a congested queue to traffic sources at end hosts that can scale to thousands of flows. We implement zD to apply backpressure in two settings: i) between TCP sources and kernel queuing discipline, and ii) between VMs as traffic sources and kernel queuing discipline in the hypervisor. zD improves throughput by up to 60%, and improves tail RTT by at least 10x at high loads, compared to standard kernel implementation. 

BGP was initially created assuming by default that all ASes are equal. Its policies and protocols, namely BGP, evolved to accommodate a hierarchical Internet, allowing an autonomous system more control over outgoing traffic than incoming traffic. However, the modern Internet is flat, making BGP asymmetrical. In particular, routing decisions are mostly in the hands of traffic sources (i.e., content providers). This leads to suboptimal routing decisions as traffic sources can only estimate route capacity at the destination (i.e., ISP). In this paper, we present the design of Unison, a system that allows an ISP to jointly optimize its intra-domain routes and inter-domain routes, in collaboration with content providers. Unison provides the ISP operator and the neighbors of the ISP with an abstraction ISP network in the form of a virtual switch. This abstraction allows the content providers to program the virtual switch with their requirements. It also allows the ISP to use that information to optimize the overall performance of its network. We show through extensive simulations that Unison can improve ISP throughput by up to 30% through cooperation with content providers. We also show that cooperation of content providers only improves performance, even for non-cooperating content providers (e.g., a single cooperating neighbour can improve ISP throughput by up to 6%). 

Packet scheduling determines the ordering of packets in a queuing data structure with respect to some ranking function that is mandated by a scheduling policy. It is the core component in many recent innovations to optimize network performance and utilization. Our focus in this paper is on the design and deployment of packet scheduling in soft-ware. Software schedulers have several advantages over hardware including shorter development cycle and flexibility in functionality and deployment location. We substantially improve current software packet scheduling performance,while maintaining flexibility, by exploiting underlying features of packet ranking; namely, packet ranks are integers and, at any point in time, fall within a limited range of values.We introduce Eiffel, a novel programmable packet scheduling system. At the core of Eiffel is an integer priority queue based on the Find First Set (FFS) instruction and designed to support a wide range of policies and ranking functions efficiently. As an even more efficient alternative, we also pro-pose a new approximate priority queue that can outperform FFS-based queues for some scenarios. To support flexibility,Eiffel introduces novel programming abstractions to express scheduling policies that cannot be captured by current, state-of-the-art scheduler programming models. We evaluate Eiffel in a variety of settings and in both kernel and userspace deployments. We show that it outperforms state of the art systems by 3-40x in terms of either number of cores utilized for network processing or number of flows given fixed processing capacity