Consider an end-user accessing two content providers, A and B, of the same type. If the end-user's ISP prioritizes A-traffic over B-traffic, the end-user may experience A-content with significantly better quality, and the ISP is said to apply "traffic differentiation." We observe that edge caching has a similar effect: if the end-user's ISP hosts a cache that serves A-content with higher hit rate than B-content, the end-user may experience A-content with significantly better quality. Hence, we examine caching as differentiation: We consider 5 popular caching providers, measure the hit rates with which they serve different content, and use the measurements to quantify the impact of edge caching on end-user Quality of Experience (QoE). We present the—in our opinion—surprising QoE disparities that result from edge caching and discuss their implications.

We explore a new network-performance metric: flow/path consistency, which captures whether the end-to-end performance of the flows that traverse a network is consistent with the aggregate performance of the network's elements. We propose a formal definition; provide preliminary evidence that---by leveraging simple math---it can be estimated with configurable confidence and minimal overhead; and argue that it could simplify network debugging.

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users' quality of experience (QoE). Most content today is accessed through edge caches, operated by cloud providers, but located near or inside the end-user's Internet Service Provider (ISP). Hence, the end-users' QoE is often determined by the interplay between the caching system (controlled by the cloud provider) and the network between edge cache and end-user (controlled by the eyeball ISP). So, we argue that an obvious point where differentiation may occur, and where transparency and neutrality may be desirable is the caching system; and that we (as a community) should perhaps consider notions of neutrality that capture the connection between caching and QoE.

Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. When determining whether a network differentiates against certain traffic, it is important to have strong evidence, especially given that traffic differentiation is illegal in certain countries. In prior work, WeHe detects differentiation via end-to-end throughput measurements between a client and server but does not isolate the network responsible for it. Differentiation can occur anywhere on the network path between endpoints; thus, further evidence is needed to attribute differentiation to a specific network. We present a system, WeHeY, built atop WeHe, that can localize traffic differentiation, i.e., obtain concrete evidence that the differentiation happened within the client's ISP. Our system builds on ideas from network performance tomography; the challenge we solve is that TCP congestion control creates an adversarial environment for performance tomography (because it can significantly reduce the performance correlation on which tomography fundamentally relies). We evaluate our system via measurements "in the wild,'' as well as in emulated scenarios with a wide-area testbed; we further explore its limits via simulations and show that it accurately localizes traffic differentiation across a wide range of network conditions. WeHeY's source code is publicly available athttps://nal-epfl.github.io/WeHeY.

Group testing enables to identify infected individuals in a population using a smaller number of tests than individual testing. To achieve this, group testing algorithms commonly assume knowledge of the number of infected individuals; nonadaptive and several adaptive algorithms fall in this category. Some adaptive algorithms, like binary splitting, operate without this assumption, but require a number of stages that may scale linearly with the size of the population. In this paper, we contribute a new algorithm that enables a balance between the number of tests and the number of stages used, and which we term diagonal splitting algorithm (DSA). Diagonal splitting, like binary splitting, does not require knowledge of the number of infected individuals, yet unlike binary splitting, is orderoptimal w.r.t. the expected number of tests it requires and is guaranteed to succeed in a small number of stages that scales at most logarithmically with the size of the population. Numerical evaluations, for diagonal splitting and a hybrid approach we propose, support our theoretical findings.

Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Despite the variety and importance of prior results, traditional work on group testing has typically assumed independent infections. However, contagious diseases among humans, like SARS-CoV-2, have an important characteristic: infections are governed by community spread, and are therefore correlated. In this paper, we explore this observation and we argue that taking into account the community structure when testing can lead to significant savings in terms of the number of tests required to guarantee a given identification accuracy. To show that, we start with a simplistic (yet practical) infection model, where the entire population is organized in (possibly overlapping) communities and the infection probability of an individual depends on the communities (s)he participates in. Given this model, we compute new lower bounds on the number of tests for zero-error identification and design community-aware group testing algorithms that can be optimal under assumptions. Finally, we demonstrate significant benefits over traditional, community-agnostic group testing via simulations using both noiseless and noisy tests. Shorter versions of this article, which contained a subset of the material, were presented in the work by Nikolopoulos et al. (2021, 2021).

We study the problem of group testing with non-identical, independent priors. So far, the pooling strategies that have been proposed in the literature take the following approach: a hand-crafted test design along with a decoding strategy is proposed, and guarantees are provided on how many tests are sufficient in order to identify all infections in a population. In this paper, we take a different, yet perhaps more practical, approach: we fix the decoder and the number of tests, and we ask, given these, what is the best test design one could use? We explore this question for the Definite Non-Defectives (DND) decoder. We formulate a (non-convex) optimization problem, where the objective function is the expected number of errors for a particular design. We find approximate solutions via gradient descent, which we further optimize with informed initialization. We illustrate through simulations that our method can achieve significant performance improvement over traditional approaches.

In this paper, we introduce a "discrete-time SIR stochastic block model" that also allows for group testing and interventions on a daily basis. Our model can be regarded as a discrete version of the well-known continuous-time SIR stochastic network model [1] and relies on a specific type of weighted graph to capture the underlying community spread. Given that infection model, we then formulate a dynamic group-testing problem by asking: (a) what is the minimum number of tests needed everyday to identify all infections? and (b) are there nonadaptive group testing strategies that achieve this with vanishing error probability? Our results show that one can leverage the knowledge of the community infection model to compute a lower bound on the number of tests and also inform nonadaptive group testing algorithms, so that they can achieve (almost) the same performance as complete individual testing with a much smaller number of tests. Moreover, these algorithms are order-optimal, under specific conditions.

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depends on the community (s)he participates in. Use cases include families, students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that if we design the testing strategy taking into account the community structure, we can significantly reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in cases where tests are noisy.

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in connected communities: each individual participates in one or more communities, and the infection probability of each individual depends on the communities (s)he participates in. Use cases include students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that making testing algorithms aware of the community structure, can significantly reduce the number of tests needed both for adaptive and non-adaptive group testing.

SIR (Susceptible, Infected or Recovered) stochastic network models are commonly used to describe the progression of epidemics inside a network. A task of interest in epidemiology is to use these models to estimate the state evolution, both at an individual as well as a population level. In this paper, we propose using continual testing to improve the state estimation at the individual level. Our testing is inspired from entropy reduction principles and requires only a small number of tests.