89 research outputs found
An Open Platform to Teach How the Internet Practically Works
Each year at ETH Zurich, around 100 students collectively build and operate
their very own Internet infrastructure composed of hundreds of routers and
dozens of Autonomous Systems (ASes). Their goal? Enabling Internet-wide
connectivity.
We find this class-wide project to be invaluable in teaching our students how
the Internet infrastructure practically works. Among others, our students have
a much deeper understanding of Internet operations alongside their pitfalls.
Besides students tend to love the project: clearly the fact that all of them
need to cooperate for the entire Internet to work is empowering.
In this paper, we describe the overall design of our teaching platform, how
we use it, and interesting lessons we have learnt over the years. We also make
our platform openly available.Comment: 6 pages, 8 figure
A path layer for the internet : enabling network operations on encrypted protocols
The deployment of encrypted transport protocols imposes new challenges for network operations. Key in-network functions such as those implemented by firewalls and passive measurement devices currently rely on information exposed by the transport layer. Encryption, in addition to improving privacy, helps to address ossification of network protocols caused by middleboxes that assume certain information to be present in the clear. However, “encrypting it all” risks diminishing the utility of these middleboxes for the traffic management tasks for which they were designed. A middlebox cannot use what it cannot see.
We propose an architectural solution to this issue, by introducing a new “path layer” for transport-independent, in-band signaling between Internet endpoints and network elements on the paths between them, and using this layer to reinforce the boundary between the hop-by-hop network layer and the end-to- end transport layer. We define a path layer header on top of UDP to provide a common wire image for new, encrypted transports. This path layer header provides information to a transport- independent on-path state machine that replaces stateful handling currently based on exposed header flags and fields in TCP; it enables explicit measurability of transport layer performance; and offers extensibility by sender-to-path and path-to-receiver communications for diagnostics and management. This provides not only a replacement for signals that are not available with encrypted traffic, but also allows integrity-protected, enhanced signaling under endpoint control. We present an implementation of this wire image integrated with the QUIC protocol, as well as a basic stateful middlebox built on Vector Packet Processing (VPP) provided by FD.io
pForest: In-Network Inference with Random Forests
The concept of "self-driving networks" has recently emerged as a possible
solution to manage the ever-growing complexity of modern network
infrastructures. In a self-driving network, network devices adapt their
decisions in real-time by observing network traffic and by performing in-line
inference according to machine learning models. The recent advent of
programmable data planes gives us a unique opportunity to implement this
vision. One open question though is whether these devices are powerful enough
to run such complex tasks?
We answer positively by presenting pForest, a system for performing
in-network inference according to supervised machine learning models on top of
programmable data planes. The key challenge is to design classification models
that fit the constraints of programmable data planes (e.g., no floating points,
no loops, and limited memory) while providing high accuracy. pForest addresses
this challenge in three phases: (i) it optimizes the features selection
according to the capabilities of programmable network devices; (ii) it trains
random forest models tailored for different phases of a flow; and (iii) it
applies these models in real time, on a per-packet basis.
We fully implemented pForest in Python (training), and in P4_16 (inference).
Our evaluation shows that pForest can classify traffic at line rate for
hundreds of thousands of flows, with an accuracy that is on-par with
software-based solutions. We further show the practicality of pForest by
deploying it on existing hardware devices (Barefoot Tofino)
Direct isolation of small extracellular vesicles from human blood using viscoelastic microfluidics
Small extracellular vesicles (sEVs; <200 nm) that contain lipids, nucleic acids, and proteins are considered promising biomarkers for a wide variety of diseases. Conventional methods for sEV isolation from blood are incompatible with routine clinical workflows, significantly hampering the utilization of blood-derived sEVs in clinical settings. Here, we present a simple, viscoelastic-based microfluidic platform for label-free isolation of sEVs from human blood. The separation performance of the device is assessed by isolating fluorescent sEVs from whole blood, demonstrating purities and recovery rates of over 97 and 87%, respectively. Significantly, our viscoelastic-based microfluidic method also provides for a remarkable increase in sEV yield compared to gold-standard ultracentrifugation, with proteomic profiles of blood-derived sEVs purified by both methods showing similar protein compositions. To demonstrate the clinical utility of the approach, we isolate sEVs from blood samples of 20 patients with cancer and 20 healthy donors, demonstrating that elevated sEV concentrations can be observed in blood derived from patients with cancer
A 3D MR-acquisition scheme for nonrigid bulk motion correction in simultaneous PET-MR.
PURPOSE: Positron emission tomography (PET) is a highly sensitive medical imaging technique commonly used to detect and assess tumor lesions. Magnetic resonance imaging (MRI) provides high resolution anatomical images with different contrasts and a range of additional information important for cancer diagnosis. Recently, simultaneous PET-MR systems have been released with the promise to provide complementary information from both modalities in a single examination. Due to long scan times, subject nonrigid bulk motion, i.e., changes of the patient's position on the scanner table leading to nonrigid changes of the patient's anatomy, during data acquisition can negatively impair image quality and tracer uptake quantification. A 3D MR-acquisition scheme is proposed to detect and correct for nonrigid bulk motion in simultaneously acquired PET-MR data. METHODS: A respiratory navigated three dimensional (3D) MR-acquisition with Radial Phase Encoding (RPE) is used to obtain T1- and T2-weighted data with an isotropic resolution of 1.5 mm. Healthy volunteers are asked to move the abdomen two to three times during data acquisition resulting in overall 19 movements at arbitrary time points. The acquisition scheme is used to retrospectively reconstruct dynamic 3D MR images with different temporal resolutions. Nonrigid bulk motion is detected and corrected in this image data. A simultaneous PET acquisition is simulated and the effect of motion correction is assessed on image quality and standardized uptake values (SUV) for lesions with different diameters. RESULTS: Six respiratory gated 3D data sets with T1- and T2-weighted contrast have been obtained in healthy volunteers. All bulk motion shifts have successfully been detected and motion fields describing the transformation between the different motion states could be obtained with an accuracy of 1.71 ± 0.29 mm. The PET simulation showed errors of up to 67% in measured SUV due to bulk motion which could be reduced to less than 10% with the proposed motion compensation approach. CONCLUSIONS: A MR acquisition scheme which yields both high resolution 3D anatomical data and highly accurate nonrigid motion information without an increase in scan time is presented. The proposed method leads to a strong improvement in both MR and PET image quality and ensures an accurate assessment of tracer uptake
Prediction of alcohol drinking in adolescents: Personality-traits, behavior, brain responses, and genetic variations in the context of reward sensitivity
Adolescence is a time that can set the course of alcohol abuse later in life. Sensitivity to reward on multiple levels is a major factor in this development. We examined 736 adolescents from the IMAGEN longitudinal study for alcohol drinking during early (mean age = 14.37) and again later (mean age = 16.45) adolescence. Conducting structural equation modeling we evaluated the contribution of reward-related personality traits, behavior, brain responses and candidate genes. Personality seems to be most important in explaining alcohol drinking in early adolescence. However, genetic variations in ANKK1 (rs1800497) and HOMER1 (rs7713917) play an equal role in predicting alcohol drinking two years later and are most important in predicting the increase in alcohol consumption. We hypothesize that the initiation of alcohol use may be driven more strongly by personality while the transition to increased alcohol use is more genetically influenced
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