Implementation and Deployment of a Distributed Network Topology Discovery Algorithm

In the past few years, the network measurement community has been interested in the problem of internet topology discovery using a large number (hundreds or thousands) of measurement monitors. The standard way to obtain information about the internet topology is to use the traceroute tool from a small number of monitors. Recent papers have made the case that increasing the number of monitors will give a more accurate view of the topology. However, scaling up the number of monitors is not a trivial process. Duplication of effort close to the monitors wastes time by reexploring well-known parts of the network, and close to destinations might appear to be a distributed denial-of-service (DDoS) attack as the probes converge from a set of sources towards a given destination. In prior work, authors of this report proposed Doubletree, an algorithm for cooperative topology discovery, that reduces the load on the network, i.e., router IP interfaces and end-hosts, while discovering almost as many nodes and links as standard approaches based on traceroute. This report presents our open-source and freely downloadable implementation of Doubletree in a tool we call traceroute@home. We describe the deployment and validation of traceroute@home on the PlanetLab testbed and we report on the lessons learned from this experience. We discuss how traceroute@home can be developed further and discuss ideas for future improvements

Inferring AS Relationships: Dead End or Lively Beginning?

Recent techniques for inferring business relationships between ASs have yielded maps that have extremely few invalid BGP paths in the terminology of Gao. However, some relationships inferred by these newer algorithms are incorrect, leading to the deduction of unrealistic AS hierarchies. We investigate this problem and discover what causes it. Having obtained such insight, we generalize the problem of AS relationship inference as a multiobjective optimization problem with node-degree-based corrections to the original objective function of minimizing the number of invalid paths. We solve the generalized version of the problem using the semidefinite programming relaxation of the MAX2SAT problem. Keeping the number of invalid paths small, we obtain a more veracious solution than that yielded by recent heuristics

Learning from My Successes and from Others' Failures: Evidence from Minimally Invasive Cardiac Surgery

Learning from past experience is central to an organization's adaptation and survival. A key dimension of prior experience is whether an outcome was successful or unsuccessful. While empirical studies have investigated the effects of success and failure in organizational learning, to date the phenomenon has received little attention at the individual level. Drawing on attribution theory in psychology, we investigate how individuals learn from their own past experiences with both failure and success and from the experiences of others. For our empirical analyses, we use ten years of data from 71 cardiothoracic surgeons who completed over 6,500 procedures using a new technology for cardiac surgery. We find that individuals learn more from their own successes than from their own failures but learn more from the failures of others than from others' successes. We also find that individuals' prior successes and others' failures can help individuals overcome their inability to learn from their own failures. Together, these findings offer both theoretical and practical insights into how individuals learn directly from their prior experience and indirectly from the experiences of others

Learning from My Success and from Others\u27 Failure: Evidence from Minimally Invasive Cardiac Surgery

Learning from past experience is central to an organization\u27s adaptation and survival. A key dimension of prior experience is whether an outcome was successful or unsuccessful. Although empirical studies have investigated the effects of success and failure in organizational learning, to date, the phenomenon has received little attention at the individual level. Drawing on attribution theory in psychology, we investigate how individuals learn from their own past experiences with both failure and success and from the experiences of others. For our empirical analyses, we use 10 years of data from 71 cardiothoracic surgeons who completed more than 6,500 procedures using a new technology for cardiac surgery. We find that individuals learn more from their own successes than from their own failures, but they learn more from the failures of others than from others\u27 successes. We also find that individuals\u27 prior successes and others\u27 failures can help individuals overcome their inability to learn from their own failures. Together, these findings offer both theoretical and practical insights into how individuals learn directly from their prior experience and indirectly from the experiences of others

Evaluation of a Large-Scale Topology Discovery Algorithm

peer reviewedIn the past few years, the network measurement community has been interested in the problem of internet topology discovery using a large number (hundreds or thousands) of measurement monitors. The standard way to obtain information about the internet topology is to use the traceroute tool from a small number of monitors. Recent papers have made the case that increasing the number of monitors will give a more accurate view of the topology. However, scaling up the number of monitors is not a trivial process. Duplication of effort close to the monitors wastes time by reexploring well-known parts of the network, and close to destinations might appear to be a distributed denial-of-service (DDoS) attack as the probes converge from a set of sources towards a given destination. In prior work, authors of this paper proposed Doubletree, an algorithm for cooperative topology discovery, that reduces the load on the network, i.e., router IP interfaces and end-hosts, while discovering almost as many nodes and links as standard approaches based on traceroute. This paper presents our open-source and freely downloadable implementation of Doubletree in a tool we call traceroute@home. We evaluate the performance of our implementation on the PlanetLab testbed and discuss a large-scale monitoring infrastructure that could benefit of Doubletree

vrfinder: Finding outbound addresses in traceroute

Current methods to analyze the Internet's router-level topology with paths collected using traceroute assume that the source address for each router in the path is either an inbound or off-path address on each router. In this work, we show that outbound addresses are common in our Internet-wide traceroute dataset collected by CAIDA's Ark vantage points in January 2020, accounting for 1.7% - 5.8% of the addresses seen at some point before the end of a traceroute. This phenomenon can lead to mistakes in Internet topology analysis, such as inferring router ownership and identifying interdomain links. We hypothesize that the primary contributor to outbound addresses is Layer 3 Virtual Private Networks (L3VPNs), and propose vrfinder, a technique for identifying L3VPN outbound addresses in traceroute collections. We validate vrfinder against ground truth from two large research and education networks, demonstrating high precision (100.0%) and recall (82.1% - 95.3%). We also show the benefit of accounting for L3VPNs in traceroute analysis through extensions to bdrmapIT, increasing the accuracy of its router ownership inferences for L3VPN outbound addresses from 61.5% - 79.4% to 88.9% - 95.5%

Task Selection and Workload: A Focus on Completing Easy Tasks Hurts Long-Term Performance

How individuals manage, organize, and complete their tasks is central to operations management. Recent research in operations focuses on how under conditions of increasing workload individuals can increase their service time, up to a point, in order to complete work more quickly. As the number of tasks increases, however, workers may also manage their workload by a different process – task selection. Drawing on research on workload, individual discretion, and behavioral decision making we theorize and then test that under conditions of increased workload individuals may choose to complete easier tasks in order to manage their load. We label this behavior Task Completion Bias (TCB). Using two years of data from a hospital emergency department we find support for TCB and also show that it improves short-term productivity. However, although it improves performance in the short-term we find that an overreliance on this task selection strategy hurts performance – as measured both by speed and revenue – in the long run. We then turn to the lab to replicate conceptually the task selection effect and show that it occurs due to the positive feelings individuals get from task completion. These findings provide an alternative mechanism for the workload-speedup effect from the literature. We also discuss implications for both research and the practice of operations in building systems to help people succeed in both the short and long run

AS relationships, customer cones, and validation

Business relationships between ASes in the Internet are typically confidential, yet knowledge of them is essential to understand many aspects of Internet structure, performance, dynamics, and evolution. We present a new algorithm to infer these relationships using BGP paths. Unlike previous approaches, our algorithm does not assume the presence (or seek to maximize the number) of valley-free paths, instead relying on three assumptions about the Internet's inter-domain structure: (1) an AS enters into a provider relationship to become globally reachable; and (2) there exists a peering clique of ASes at the top of the hierarchy, and (3) there is no cycle of p2c links. We assemble the largest source of validation data for AS-relationship inferences to date, validating 34.6% of our 126,082 c2p and p2p inferences to be 99.6% and 98.7% accurate, respectively. Using these inferred relationships, we evaluate three algorithms for inferring each AS's customer cone, defined as the set of ASes an AS can reach using customer links. We demonstrate the utility of our algorithms for studying the rise and fall of large transit providers over the last fifteen years, including recent claims about the flattening of the AS-level topology and the decreasing influence of "tier-1" ASes on the global Internet

Monitoring Cognitive and Emotional Processes Through Pupil and Cardiac Response During Dynamic Versus Logical Task

The paper deals with the links between physiological measurements and cognitive and emotional functioning. As long as the operator is a key agent in charge of complex systems, the definition of metrics able to predict his performance is a great challenge. The measurement of the physiological state is a very promising way but a very acute comprehension is required; in particular few studies compare autonomous nervous system reactivity according to specific cognitive processes during task performance and task related psychological stress is often ignored. We compared physiological parameters recorded on 24 healthy subjects facing two neuropsychological tasks: a dynamic task that require problem solving in a world that continually evolves over time and a logical task representative of cognitive processes performed by operators facing everyday problem solving. Results showed that the mean pupil diameter change was higher during the dynamic task; conversely, the heart rate was more elevated during the logical task. Finally, the systolic blood pressure seemed to be strongly sensitive to psychological stress. A better taking into account of the precise influence of a given cognitive activity and both workload and related task-induced psychological stress during task performance is a promising way to better monitor operators in complex working situations to detect mental overload or pejorative stress factor of error