TCP remains the dominant transport protocol for Internet traffic, but the preponderance of its congestion control mechanisms in determining flow throughput is often disputed. This paper analyzes the extent to which network, host and application settings define flow throughput over time and across autonomous systems. Drawing from a longitudinal study spanning five years of passive traces collected from a single transit link, our results show that continuing OS upgrades have reduced the influence of host limitations owing both to windowscale deployment, which by 2011 covered 80% of inbound traffic, and increased socket buffer sizes. On the other hand, we show that for this data set, approximately half of all inbound traffic remains throttled by constraints beyond network capacity, challenging the traditional model of congestion control in TCP traffic as governed primarily by loss and delay.

Existing methods for traffic resilience at the network and transport layers typically work in isolation, often resorting to inference in fault detection and recovery respectively. This both duplicates functionality across layers, eroding efficiency, and leads to protracted recovery cycles, affecting responsiveness. Such misalignment is particularly at odds with the unprecedented concentration of traffic in data-centers, in which network and hosts are managed in unison. This paper advocates instead a cross-layer approach to traffic resilience. The proposed architecture, INFLEX, builds on the abstractions provided by software-defined networking (SDN) to maintain multiple virtual forwarding planes which the network assigns to flows. In case of path failure, transport protocols pro-actively request to switch plane in a manner which is unilaterally deployable by an edge domain, providing scalable end-to-end forwarding path resilience.

This paper describes TARDIS (Traffic Assignment and Retiming Dynamics with Inherent Stability) which is an algorithmic procedure designed to reallocate traffic within Internet Service Provider (ISP) networks. Recent work has investigated the idea of shifting traffic in time (from peak to off-peak) or in space (by using different links). This work gives a unified scheme for both time and space shifting to reduce costs. Particular attention is given to the commonly used 95th percentile pricing scheme.

This paper considers fundamental measurements which drive TCP flows: throughput, RTT and loss. It is clear that throughput is, in some sense, a function of both RTT and loss. In their seminal paper Padyhe et al [1] begin with a mathematical model of the TCP sliding window evolution process and come up with an equation showing that TCP throughput is (roughly) proportional to 1/RTT√p where p is the probability of packet loss. Their equation is shown to be consistent with data gathered on several links. This paper takes the opposite approach and analyses a large number of packet traces from well-known sources in order to create a data-driven estimate of the functions which relate TCP, loss and RTT. Regression analysis is used to fit models to connect the quantities. The fitted models show different behaviour from that expected in [1].

When designing distributed systems and Internet protocols, designers can benefit from statistical models of the Internet that can be used to estimate their performance. However, it is frequently impossible for these models to include every property of interest. In these cases, model builders have to select a reduced subset of network properties, and the rest will have to be estimated from those available. In this paper we present a technique for the analysis of Internet round trip times (RTT) and its relationship with other geographic and network properties. This technique is applied on a novel dataset comprising ~19 million RTT measurements derived from ~200 million RTT samples between ~54 thousand DNS servers. Our main contribution is an information-theoretical analysis that allows us to determine the amount of information that a given subset of geographic or network variables (such as RTT or great circle distance between geolocated hosts) gives about other variables of interest. We then provide bounds on the error that can be expected when using statistical estimators for the variables of interest based on subsets of other variables.

Explicit congestion control (XCC) is emerging as one potential solution for overcoming limitations inherent to the current TCP algorithm, characterized by unstable throughput, high queuing delay, RTT-limited fairness, and a static dynamic range that does not scale well to high bandwidth delay product networks. In XCC, routers provide multibit feedback to sources, which, in turn, adapt throughput more accurately to the path bandwidth with potentially faster convergence times. Such systems, however, require precise knowledge of link capacity for efficient operation. In the presence of variable-capacity media, e.g., 802.11, such information is not entirely obvious or may be difficult to extract. We explore three possible algorithms for XCC which retain efficiency under such conditions by inferring available bandwidth from queue dynamics and test them through simulations with two relevant XCC protocols: XCP and RCP. Additionally, preliminary results from an experimental implementation based on XCP are presented. Finally, we compare our proposals with TCP and show how such algorithms outperform it in terms of efficiency, stability, queuing delay, and flow-rate fairness.