Despite 40 years of active research on congestion control, there has been little or no consideration of how it impacts the energy usage of end-hosts or networking equipment. Particularly with the burgeoning energy consumption of data centers and wide-area networks, we argue that the time is ripe for the networking community to start thinking along these lines. To pave the way, we conduct lab experiments to measure the energy used by popular congestion control algorithms. We consider various aspects of congestion control and the rich research challenges that arise when we consider energy efficiency. Specifically, we find that fairness for the bandwidth allocated by congestion control can increase energy consumption by as high as 16%. We extrapolate these results to projected savings on the order of $10 million/year for large data centers.

On-demand streaming video traffic is managed by an adaptive bi-trate (ABR) algorithm whose job is to optimize quality of experience (QoE) for a single video session. ABR algorithms leave the question of sharing network resources up to transport-layer algorithms. We observe that as the internet gets faster relative to video streaming rates, this delegation of responsibility gives video traffic a burstier on-off traffic pattern. In this paper, we show we can substantially smooth video traffic to improve its interactions with the rest of the internet, while maintaining the same or better QoE for streaming video. We smooth video traffic with two design principles: application-informed pacing, which allows ABR algorithms to set an upper limit on packet-by-packet throughput, and by designing ABR algorithms that work with pacing. We propose a joint ABR and rate-control scheme, called Sammy, which selects both video quality and pacing rates. We implement our scheme and evaluate it at a large video streaming service. Our approach smooths video, making it a more friendly neighbor to other internet applications. One surprising result is that being friendlier requires no compromise for the video traffic: in large scale, production experiments, Sammy improves video QoE over an existing, extensively tested and tuned production ABR algorithm.

When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly allocated between a treatment group, which uses the new algorithm, and a control group, which uses the existing algorithm. However, because networks are congested, both treatment and control traffic compete against each other for resources in a way that biases the outcome of these tests. This bias can have a surprisingly large effect; for example, in lab A/B tests with two widely used congestion control algorithms, the treatment appeared to deliver 150% higher throughput when used by a few flows, and 75% lower throughput when used by most flows---despite the fact that the two algorithms have identical throughput when used by all traffic.

The coded trace reconstruction problem asks to construct a code C ⊂ {0,1}nsuch that any x ∈ C is recoverable from independent outputs (“traces”) of x from a binary deletion channel (BDC). We present binary codes of rate 1-ε that are efficiently recoverable from exp(Oq(log1/3([1/(ε)]))) (a constant independent of n) traces of a BDCq for any constant deletion probability q ∈ (0,1). We also show that, for rate 1 -ε binary codes, ~Ω(log5/2(1/ε)) traces are required. The results follow from a pair of black-box reductions that show that average-case trace reconstruction is essentially equivalent to coded trace reconstruction. We also show that there exist codes of rate 1 -ε over an Oε(1)-sized alphabet that are recoverable from O(log(1/ε)) traces, and that this is tight.

In network tomography, one goal is to identify a small set of failed links in a network using as little information as possible. One way of setting up this problem is called graph-constrained group testing. Graph-constrained group testing is a variant of the classical combinatorial group testing problem, where the tests that one is allowed are additionally constrained by a graph. In this case, the graph is given by the underlying network topology. The main contribution of this work is to show that for most graphs, the constraints imposed by the graph are no constraint at all. That is, the number of tests required to identify the failed links in graph-constrained group testing is near-optimal even for the corresponding group testing problem with no graph constraints. Our approach is based on a simple randomized construction of tests. To analyze our construction, we prove new results about the size of giant components in randomly sparsified graphs. Finally, we provide empirical results which suggest that our connected-subgraph tests perform better not just in theory but also in practice, and in particular perform better on a real-world network topology.

Large organizations often have users in multiple sites which are connected over the Internet. Since resources are limited, communication between these sites needs to be carefully orchestrated for the most benefit to the organization. We present a Mission-optimized Overlay Network (MON), a hybrid overlay network architecture for maximizing utility to the organization. We combine an offline and an online system to solve non-concave utility maximization problems. The offline tier, the Predictive Flow Optimizer (PFO), creates plans for routing traffic using a model of network conditions. The online tier, MONtra, is aware of the precise local network conditions and is able to react quickly to problems within the network. Either tier alone is insufficient. The PFO may take too long to react to network changes. MONtra only has local information and cannot optimize non-concave mission utilities. However, by combining the two systems, MON is robust and achieves near-optimal utility under a wide range of network conditions. While best-effort overlay networks are well studied, our work is the first to design overlays that are optimized for mission utility.