Discovering Network Neighborhoods Using Peer-to-Peer Lookups

In many distributed applications, end hosts need to know the network locations of other nearby participating hosts in order to enhance overall performance. Potential applications that can benefit from the location information include automatic selection of nearby Web servers, proximity routing in a peer-to-peer system, and loss recovery in reliable multicasting. We focus in this paper on the network neighborhood discovery problem in large-scale distributed systems. In these systems, the number of participating nodes can be very large, and the membership can dynamically change. Our goal is for each node to discover other "nearby" participating nodes in a completely decentralized manner, where each node probes only a small subset of other nodes in the system. This approach will lead to improved overall performance by matching client requests for services with participants in the peer-to-peer service system that are, on average, nearby in the network sense. Recent works in distributed peer-to-peer systems, such as Chord, CAN, Tapestry and Pastry, provide efficient distributed lookup structures. In this paper, we investigate a rendezvous-based scheme for a node to discover other nearby participating nodes using a peer-to-peer lookup system such as Chord. Given a key, the Chord protocol maps the key onto a node. Our idea for network neighborhood discovery is for each host to compute a key that characterizes its network location on the Internet. We call such a key the location key, and the nodes that these location keys are mapped to the Rendezvous Points. To lookup other nearby participating nodes, a node seeking some service queries its corresponding rendezvous point using its location key. We focus on the issue of how to generate the location key in a distributed fashion such that nodes that are close to each other in the actual network will have similar location key values, and therefore be mapped to nearby locations on the Chord ring. In this paper, we examine the performance tradeoffs of such a rendezvous scheme using the Global Network Positioning (GNP) approach to generate the location keys. In GNP, each node measures its network distances to a few landmark nodes to derive its coordinates in a D-dimensional geometric space. We generate a host's Chord location key from its 1-dimensional GNP coordinate, and use coordinates from a higher dimensional space to refine the searching process for the closest node. We evaluate our scheme in the context of the nearest neighbor discovery problem. Using data from the Active Measurement Project of the National Laboratory for Applied Network Research (NLANR), we compare its performance with a random mapping scheme, where location keys are randomly generated. Using our coordinate-based rendezvous scheme, 66% of the nodes found their actual closest network neighbor by pinging only a small number of nodes.Singapore-MIT Alliance (SMA

PALM: Predicting Internet Network Distances Using Peer-to-Peer Measurements

Landmark-based architecture has been commonly adopted in the networking community as a mechanism to measure and characterize a host's location on the Internet. In most existing landmark based approaches, end hosts use the distance measurements to a common, fixed set of landmarks to derive an estimated location on the Internet. This paper investigates whether it is possible for participating peer nodes in an overlay network to collaboratively construct an accurate geometric model of its topology in a completely decentralized peer-to-peer fashion, without using a fixed set of landmarks. We call such a peer-to-peer approach in topology discovery and modeling using landmarks PALM (Peers As LandMarks). We evaluate the performance characteristics of such a decentralized coordinates-based approach under several factors, including dimensionality of the geometric space, peer distance distribution, and the number of peer-to-peer distance measurements used. We evaluate two PALM-based schemes: RAND-PALM and ISLAND. In RAND-PALM, a peer node randomly selects from existing peer nodes as its landmarks. In ISLAND (Intelligent Selection of Landmarks), each peer node selects its landmarks by exploiting the topological information derived based on existing peer nodes' coordinates values.Singapore-MIT Alliance (SMA

A decentralized network coordinate system for robust internet distance

Abstract-Network coordinate systems have recently been developed as a scalable mechanism to predict latencies among arbitrary Internet hosts. Our research addresses several design challenges of a large-scale decentralized network coordinate system that were not fully addressed in prior work. In particular, we examine the design issues of a decentralized network coordinate system operating in a peer-to-peer network with high churn, high fractions of faulty or misbehaving peers, and high degrees of network path anomalies. This paper presents a fully decentralized network coordinate system, PCoord, for robust and fault-tolerant Internet distance prediction. Through extensive simulations, we examine the convergence behavior and prediction accuracy of PCoord under a variety of scenarios, and compare its performance with an existing network coordinate system, Vivaldi. Our results indicate that PCoord is robust under high churn, and degrades gracefully even under high fractions of faulty nodes, and high degrees of triangle inequality violations in the underlying network distances. Finally, our results indicate that even under an extremely challenging flash-crowd scenario where 1740 hosts simultaneously join the system, PCoord is able to converge to 12% median relative prediction error within 10 seconds

A Decentralized Network Coordinate System for Robust Internet Distance

Network distance, measured as round-trip latency be-tween hosts, is important for the performance of many In-ternet applications. For example, nearest server selection and proximity routing in peer-to-peer networks rely on the ability to select nodes based on inter-host latencies. This paper presents PCoord, a decentralized network coordi-nate system for Internet distance prediction. In PCoord, the network is modeled as a D-dimensional geometric space; each host computes its coordinates in this geometric space to characterize its network location based on a small num-ber of peer-to-peer network measurements. The goal is to embed hosts in the geometric space so that the Euclidean distance between two hosts ’ coordinates accurately predicts their actual inter-host network latency. PCoord constructs network coordinates in a fully decentralized fashion. We present several mechanisms in PCoord to stabilize the sys-tem convergence. Our simulation results using real Internet measurements suggest that, even under an extremely chal-lenging flash-crowd scenario where 1740 hosts simultane-ously join the system, PCoord with a 5-dimensional Eu-clidean model is able to converge to 11 % median prediction error in 10 coordinate updates per host on average.

Decentralized network coordinate system for Internet distance prediction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 163-168).Several recently emerged Internet services make use of application-level or overlay networks. Examples of such services include overlay multicast, structured peer-to- peer lookup services, and peer-to-peer file sharing. Many of these services could benefit from enabling participating end hosts to estimate their relative network locations within the overlay. In this thesis, we present PCoord, a peer-to-peer network coordinate system for overlay topology discovery and distance prediction. The goal of PCoord is to allow participating peer nodes in an overlay network to collaboratively construct an accurate geometric model of the overlay network topology in a completely decentralized peer-to-peer fashion. We evaluate the PCoord approach through extensive simulations using both real network measurements and simulated topologies. Our simulation results indicate that PCoord can embed hosts in a low dimensional Euclidean model with a small median prediction error.by Li-wei H. Lehman.Ph.D

Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort

Patients in critical care often exhibit complex disease patterns. A fundamental challenge in clinical research is to identify clinical features that may be characteristic of adverse patient outcomes. In this work, we propose a data-driven approach for phenotype discovery of patients in critical care. We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically discover the latent "topic" structure of diseases, symptoms, and findings documented in hospital discharge summaries. We show that the latent topic structure can be used to reveal phenotypic patterns of diseases and symptoms shared across subgroups of a patient cohort, and may contain prognostic value in stratifying patients' post hospital discharge mortality risks. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluate the clinical utility of the discovered topic structure in identifying patients who are at high risk of mortality within one year post hospital discharge. We demonstrate that the learned topic structure has statistically significant associations with mortality post hospital discharge, and may provide valuable insights in defining new feature sets for predicting patient outcomes.National Institutes of Health (U.S.) (Grant R01-EB001659)National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01GM104987

Methods of Blood Pressure Measurement in the ICU*

OBJECTIVE:: Minimal clinical research has investigated the significance of different blood pressure monitoring techniques in the ICU and whether systolic vs. mean blood pressures should be targeted in therapeutic protocols and in defining clinical study cohorts. The objectives of this study are to compare real-world invasive arterial blood pressure with noninvasive blood pressure, and to determine if differences between the two techniques have clinical implications. DESIGN:: We conducted a retrospective study comparing invasive arterial blood pressure and noninvasive blood pressure measurements using a large ICU database. We performed pairwise comparison between concurrent measures of invasive arterial blood pressure and noninvasive blood pressure. We studied the association of systolic and mean invasive arterial blood pressure and noninvasive blood pressure with acute kidney injury, and with ICU mortality. SETTING:: Adult intensive care units at a tertiary care hospital. PATIENTS:: Adult patients admitted to intensive care units between 2001 and 2007. INTERVENTIONS:: None. MEASUREMENTS AND MAIN RESULTS:: Pairwise analysis of 27,022 simultaneously measured invasive arterial blood pressure/noninvasive blood pressure pairs indicated that noninvasive blood pressure overestimated systolic invasive arterial blood pressure during hypotension. Analysis of acute kidney injury and ICU mortality involved 1,633 and 4,957 patients, respectively. Our results indicated that hypotensive systolic noninvasive blood pressure readings were associated with a higher acute kidney injury prevalence (p = 0.008) and ICU mortality (p < 0.001) than systolic invasive arterial blood pressure in the same range (≤70 mm Hg). Noninvasive blood pressure and invasive arterial blood pressure mean arterial pressures showed better agreement; acute kidney injury prevalence (p = 0.28) and ICU mortality (p = 0.76) associated with hypotensive mean arterial pressure readings (≤60 mm Hg) were independent of measurement technique. CONCLUSIONS:: Clinically significant discrepancies exist between invasive and noninvasive systolic blood pressure measurements during hypotension. Mean blood pressure from both techniques may be interpreted in a consistent manner in assessing patients' prognosis. Our results suggest that mean rather than systolic blood pressure is the pre ferred metric in the ICU to guide therapy.National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01EB001659

Phenotyping hypotensive patients in critical care using hospital discharge summaries

Among critically-ill patients, hypotension represents a failure in compensatory mechanisms and may lead to organ hypoperfusion and failure. In this work, we adopt a datadriven approach for phenotype discovery and visualization of patient similarity and cohort structure in the intensive care unit (ICU). We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically learn a d-dimensional feature representation of patients that captures the latent 'topic' structure of diseases, symptoms, medications, and findings documented in hospital discharge summaries. We then used the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to convert the d-dimensional latent structure learned from HDP into a matrix of pairwise similarities for visualizing patient similarity and cohort structure. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluated the clinical utility of the discovered topic structure in phenotyping critically-ill patients who experienced hypotensive episodes. Our results indicate that the approach is able to reveal clinically interpretable clustering structure within our cohort and may potentially provide valuable insights to better understand the association between disease phenotypes and outcomes.National Institutes of Health (U.S.) (Grant R01-EB017205)National Institutes of Health (U.S.) (Grant R01-EB001659)National Institutes of Health (U.S.) (Grant R01GM104987