Abstract

International audience; The study of round-trip time (RTT) measurements on the Internet is of particular importance for improving real-time applications, enforcing QoS with traffic engineering, or detecting unexpected network conditions. On large timescales, from 1 hour to several days, RTT measurements exhibit characteristic patterns due to inter and intra-AS routing changes and traffic engineering, in addition to link congestion. We propose the use of a nonparametric Bayesian model, the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), to characterize RTT timeseries. The parameters of the HMM, including the number of states, as well as the values of hidden states are estimated from delay observations by Gibbs sampling. No assumptions are made on the number of states, and a nonparametric mixture model is used to represent a wide range of delay distribution in each state for more flexibility. We validate the model through three applications: on RIPE Atlas measurements we show that 80% of the states learned on RTTs match only one AS path; on a labelled delay changepoint dataset we show that the model is competitive with state-of-the-art changepoint detection methods in terms of precision and recall; and we show that the predictive ability of the model allows us to reduce the monitoring cost by 90% in routing overlays using Markov decision processes.

Abstract

In the context of the ERAINT project, a number of human-in-the-loop simulations were conducted to study the implications of integrating a remote piloted aircraft system (RPAS) into the managed airspace. For the purpose of this study, the RPAS was assumed to be involved in surveillance missions, flying a large-endurance scan pattern. The area of surveillance was selected such that it crossed an active airway for approaches. Furthermore, the simulations also included situations in which the RPAS was involved in an emergency situation such as lost links and engine failures. From previous work, the results obtained from these simulations showed that the air traffic controllers (ATCs) could successfully manage the required separations for airspace safety assurance. Nevertheless, the number of total commands issued increased, in particular the number of requirements for altitude changes, and especially those destined to commercial aircraft. Given an aircraft's flying altitude impact on performance, one question rapidly arose: Is there an increase in flight costs for airlines as a result of the increased number of ATC commands issued to provide the necessary separation with the RPAS? For this purpose two metrics relating to time and fuel are defined such that they are targeted on quantifying the economic impact for commercial air traffic resulting from the presence of a RPAS. Both metrics are computed from the ADS-B traces of all aircraft in the sector and the results of each simulation are compared with those of a baseline simulation, in which the RPAS is not present. To improve on the comparison between each simulation's results we complement this study with a statistical analysis of the available data samples using paired t-test analyses to determine if the observed differences are statistically significant or simply due to random variability. Peer Reviewed

Abstract

Conference of 10th IEEE International Symposium on Industrial Embedded Systems, SIES 2015 ; Conference Date: 8 June 2015 Through 10 June 2015; Conference Code:116797; International audience; Many-core architectures are promising hardware to design hard real-time systems as they are based on simpler and thus more predictable processors than multi-core systems. However, the worst-case behavior of the Network-on-Chip (NoC) for both inter-core and core to external memories or peripherals communications must be established. Several NoCs targeting hard real-time systems, made of specific hardware extensions, have been designed. However, none of these extensions are currently available in commercially existing NoC-based many-core architectures, that instead rely on wormhole switching with round-robin arbitration. In this paper, we thus demonstrate three properties of such NoC-based wormhole networks to identify worst-case scenarios and reduce the pessimism when modeling flows in contentions. We then describe and evaluate an algorithm to compute Worst-Case Traversal Time (WCTT) of flows that uses these properties. In particular, our results show that the pessimism can be reduced up to 50% compared to current state-of-the-art real-time packet schedulability analysis.

Abstract

Recent developments in Mobility-on-Demand (MoD) systems have demonstrated the potential of road vehicles as an efficient mode of urban transportation Newly developed algorithms can compute vehicle routes in real-time for batches of requests and allow for multiple requests to share vehicles. These algorithms have primarily focused on optimally producing vehicle schedules to pick up and drop off requests. The redistribution of idle vehicles to areas of high demand, known as rebalancing, on the contrary has received little attention in the context of ride-sharing. In this paper, we present a method to rebalance idle vehicles in a ride-sharing enabled MoD fleet. This method consists of an algorithm to optimally partition the fleet operating area into rebalancing regions, an algorithm to determine a real-time demand estimate for every region using incoming requests, and an algorithm to optimize the assignment of idle vehicles to these rebalancing regions using an integer linear program. Evaluation with historical taxi data from Manhattan shows that we can service 99.8% of taxi requests in Manhattan using 3000 vehicles with an average waiting time of 57.4 seconds and an average in-car delay of 13.7 seconds. Moreover, we can achieve a higher service rate using 2000 vehicles than prior work achieved with 3000. Furthermore, with a fleet of 3000 vehicles, we reduce the average travel delay by 86%, the average waiting time by 37%, and the amount of ignored requests by 95% compared to earlier work at the expense of an increased distance travelled by the fleet.

Abstract

Recent developments in Mobility-on-Demand (MoD) systems have demonstrated the potential of road vehicles as an efficient mode of urban transportation Newly developed algorithms can compute vehicle routes in real-time for batches of requests and allow for multiple requests to share vehicles. These algorithms have primarily focused on optimally producing vehicle schedules to pick up and drop off requests. The redistribution of idle vehicles to areas of high demand, known as rebalancing, on the contrary has received little attention in the context of ride-sharing. In this paper, we present a method to rebalance idle vehicles in a ride-sharing enabled MoD fleet. This method consists of an algorithm to optimally partition the fleet operating area into rebalancing regions, an algorithm to determine a real-time demand estimate for every region using incoming requests, and an algorithm to optimize the assignment of idle vehicles to these rebalancing regions using an integer linear program. Evaluation with historical taxi data from Manhattan shows that we can service 99.8% of taxi requests in Manhattan using 3000 vehicles with an average waiting time of 57.4 seconds and an average in-car delay of 13.7 seconds. Moreover, we can achieve a higher service rate using 2000 vehicles than prior work achieved with 3000. Furthermore, with a fleet of 3000 vehicles, we reduce the average travel delay by 86%, the average waiting time by 37%, and the amount of ignored requests by 95% compared to earlier work at the expense of an increased distance travelled by the fleet.

Abstract

hieving energy efficiency has recently become an essential aim of networking research due to the ever increasing power consumption and CO2 emissions generated by large data networks. For this problem, the emerging paradigm of Software-Defined Networks (SDN) can be seen as an attractive solution. In these networks an energy-aware routing model could be easily implemented leveraging the control and data plane separation. This paper addresses the problem of optimizing the power consumption in SDN using an energy-aware traffic engineering approach that minimizes the number of links that can be used to satisfy a given traffic demand. Different from previous works, we focus on optimizing energy consumption in OpenFlow networks with in-band control traffic. Our approach also considers performance constraints that are crucial in the correct operation of SDN, such as bounded delay for the control plane traffic and load balance between controllers. First, we present a complete formulation of the optimization problem involving the routing requirements for control and data plane communications. To reduce the time complexity of our model in large-scale topologies we derive a heuristic algorithm. Significant values of energy saving (up to 60%) are reached in the simulations using real topologies and demands data. Peer Reviewed

Abstract

This paper reports on the performance of signalised intersection control using vehicle GPS information compared to fixed-time and inductive loop based control. Traffic congestion forecasts estimate an increase of about 60% in 2030. At present, poor choice of signal timings by isolated intersection controllers cause traffic delays that have enormous negative impacts on the economy and environment. Signal timings can be improved by using vehicles' GPS information to overcome the control action deficit at isolated intersections. This new signal control algorithm is beneficial for traffic engineers and governmental agencies, as traffic flow can be optimised and, hence, fuel consumption and emissions decreased. Under the open European Telecommunication Standards Institute (ETSI) Cooperative Awareness Message (CAM) framework, a traffic responsive GPS based vehicle actuation algorithm (GPS-VA) is proposed. GPS-VA uses position and heading data from vehicle status broadcasts, and inferred velocity information to determine vehicle queue lengths and detect vehicles passing through the intersection. The gathered information is then used to actuate intersection signal timings. Microscopic simulations comparing GPS-VA to fixed-time control and inductive loop based vehicle actuation (Loop-VA) on four urban road networks were performed to see how the proposed GPS-VA algorithm performs compared to existing control strategies. The results show that the GPS-VA is an effective alternative to traditional intersection control strategies, offering delay reductions of up to 50% for connected vehicle fleet penetrations above 30%.

Abstract

The overall punctuality of the railway system is of particular importance for customers in order to use trains as a means of public transport. Investigating the drivers of low punctuality of trains is an essential step for improving their punctuality. Severe weather conditions are assumed to be one of the drivers of delays and low punctuality of the transportation system. This paper addresses the extent to which the weather factors such as temperature, snow, precipitation and wind influence the punctuality of trains on the Norwegian railways. The data for the study are collected from the passenger trains on the Nordland Line. The study period is 10 years, from the beginning of 2007 to the end of 2016. Data are analysed on a daily and weekly basis, and correlation and regression analyses are used to perform the statistical analysis. The results show that extreme cold weather during the winters is a crucial influencing factor contributing to delays and low punctuality. We also found that the weather variable that best explains variations in punctuality of passenger trains on this line is snow depth. © The Author(s) 2018 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) Document type: Article