Control versus Data Flow in Parallel Database Machines

The execution of a query in a parallel database machine can be controlled in either a control flow way, or in a data flow way. In the former case a single system node controls the entire query execution. In the latter case the processes that execute the query, although possibly running on different nodes of the system, trigger each other. Lately, many database research projects focus on data flow control since it should enhance response times and throughput. The authors study control versus data flow with regard to controlling the execution of database queries. An analytical model is used to compare control and data flow in order to gain insights into the question which mechanism is better under which circumstances. Also, some systems using data flow techniques are described, and the authors investigate to which degree they are really data flow. The results show that for particular types of queries data flow is very attractive, since it reduces the number of control messages and balances these messages over the node

Acceptability of road pricing and revenue use in the Netherlands

It is generally acknowledged that the implementation of other, more efficient, road pricing measures meet public resistance and that acceptability is nowadays one of the major barriers to successful implementation. Despite the fact that politicians and the public regard transport problems as very urgent and important, people do have concerns about road pricing, resulting in low acceptance levels. This paper presents the empirical results of a questionnaire among Dutch commuters regularly facing congestion asking for their opinion (in terms of acceptance) on road pricing measures and revenue use targets. We find that road pricing is in general not very acceptable and that revenue use is important for the explanation of the level of acceptance. Road pricing is more acceptable when revenues are used to replace existing car taxation or to lower fuel taxes. Moreover, personal characteristics of the respondent have an impact on support levels. Higher educated people, as well as respondents with a higher value of time and with higher perceived effectiveness of the measure, seem to find road pricing measures more acceptable than other people. The same holds for people that receive financial support for their commuting costs and for respondents driving many kilometers in a year. When we ask directly for the acceptability of different types of revenue use (not part of a road pricing measure), again abandoning of existing car (ownership) taxes receives most support whereas the general budget is not acceptable.

Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation

The typical method to couple activity-based demand generation (ABDG) and dynamic traffic assignment (DTA) is time-dependent origin-destination (O-D) matrices. With that coupling method, the individual traveler's information gets lost. Delays at one trip do not affect later trips. However, it is possible to retain the full agent information from the ABDG by writing out all agents' plans, instead of the O-D matrix. A plan is a sequence of activities, connected by trips. Because that information typically is already available inside the ABDG, this is fairly easy to achieve. Multiagent simulation (MATSim) takes such plans as input. It iterates between the traffic flow simulation (sometimes called network loading) and the behavioral modules. The currently implemented behavioral modules are route finding and time adjustment. Activity resequencing or activity dropping are conceptually clear but not yet implemented. Such a system will react to a time-dependent toll by possibly rearranging the complete day; in consequence, it goes far beyond DTA (which just does route adaptation). This paper reports on the status of the current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to the authors' knowledge, this is the first time traveler-based information (and not just O-D matrices) is taken from an ABDG and used in a MATSim. The simulation results are compared with real-world traffic counts from about 100 measurement stations

Translation elongation can control translation initiation on eukaryotic mRNAs

Synonymous codons encode the same amino acid, but differ in other biophysical properties. The evolutionary selection of codons whose properties are optimal for a cell generates the phenomenon of codon bias. Although recent studies have shown strong effects of codon usage changes on protein expression levels and cellular physiology, no translational control mechanism is known that links codon usage to protein expression levels. Here, we demonstrate a novel translational control mechanism that responds to the speed of ribosome movement immediately after the start codon. High initiation rates are only possible if start codons are liberated sufficiently fast, thus accounting for the observation that fast codons are overrepresented in highly expressed proteins. In contrast, slow codons lead to slow liberation of the start codon by initiating ribosomes, thereby interfering with efficient translation initiation. Codon usage thus evolved as a means to optimise translation on individual mRNAs, as well as global optimisation of ribosome availability

Effectiveness and acceptability of congestion pricing

2015 Spring.Includes bibliographical references.Urban congestion is a pervasive and growing problem in developed and developing countries. The lack of excludability for scarce urban space, specifically roads and parking spaces, creates a common resource problem yielding a congestion externality that generates many external costs. Marginal social cost pricing has long been advocated as a means of alleviating market failures resulting from such negative (environmental) externalities. Congestion pricing comes in numerous forms (e.g., tolls on roads or express lanes), but has only been sporadically adopted despite congestion being a growing problem. The literature argues that concerns on equity and fairness issues and revenue redistribution are major hurdles of making an effective congestion pricing policy politically feasible and publicly acceptable. This dissertation investigates the effectiveness and acceptability of congestion pricing schemes in different contexts and examines whether individual beliefs in addition to the objective welfare effects determine voter acceptability. The first chapter employs laboratory experiments to examine the evolution of voting behavior after individuals become accustomed to the congestion problem and the congestion pricing policy, and the nature of the experience from the congestion policy. The congestion pricing policy exogenously creates inequitable outcomes which in some cases makes some people worse off. The second chapter develops and examines a three-player bottleneck congestion game and examines the ex-ante and ex-post welfare implications of an \textit{ex-ante} efficient tolling policy. The third chapter examines the effectiveness and acceptability of tolls in the three-player bottleneck congestion game using laboratory experiments where equity concerns are endogenously determined. The results suggest policymakers should be open to and considerate of the equity effects, the characteristics and beliefs of their constituents, and how to earmark revenues before implementing efficiency enhancing environmental policies

PoissonProb: A new rate-based available bandwidth measurement algorithm.

Accurate available bandwidth measurement is important for network protocols and distributed programs design, traffic optimization, capacity planning, and service verification. Research on measuring available bandwidth falls into two basic classes: the network traffic modeling algorithms and the self-induced algorithms. The self-induced algorithms are based on packet dispersion techniques. The currently available bandwidth measurement algorithms face the problems of distortion of measurement on multi-hop paths, system resource limitations, probe traffic intrusiveness and measurement accuracy. We have developed a new rate-based self-induced algorithm---PoissonProb. The intervals between probe packets of this algorithm are in Poisson distribution format and the algorithm infers the available bandwidth according to the average of probe packets rate. The algorithm has been implemented as the PoissonProb Available Bandwidth (PAB) measurement tool. The PAB tool can be operated in either sender-based or receiver-based mode. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .X56. Source: Masters Abstracts International, Volume: 44-03, page: 1418. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Machine learning-based available bandwidth estimation

Today’s Internet Protocol (IP), the Internet’s network-layer protocol, provides a best-effort service to all users without any guaranteed bandwidth. However, for certain applications that have stringent network performance requirements in terms of bandwidth, it is significantly important to provide Quality of Ser- vice (QoS) guarantees in IP networks. The end-to-end available bandwidth of a network path, i.e., the residual capacity that is left over by other traffic, is deter- mined by its tight link, that is the link that has the minimal available bandwidth. The tight link may differ from the bottleneck link, i.e., the link with the minimal capacity. Passive and active measurements are the two fundamental approaches used to estimate the available bandwidth in IP networks. Unlike passive measurement tools that are based on the non-intrusive monitoring of traffic, active tools are based on the concept of self-induced congestion. The dispersion, which arises when packets traverse a network, carries information that can reveal relevant network characteristics. Using a fluid-flow probe gap model of a tight link with First-in, First-out (FIFO) multiplexing, accepted probing tools measure the packet dispersion to estimate the available bandwidth. Difficulties arise, how- ever, if the dispersion is distorted compared to the model, e.g., by non-fluid traffic, multiple tight links, clustering of packets due to interrupt coalescing and inaccurate time-stamping in general. It is recognized that modeling these effects is cumbersome if not intractable. To alleviate the variability of noise-afflicted packet gaps, the state-of-the-art bandwidth estimation techniques use post-processing of the measurement results, e.g., averaging over several packet pairs or packet trains, linear regression, or a Kalman filter. These techniques, however, do not overcome the basic as- sumptions of the deterministic fluid model. While packet trains and statistical post-processing help to reduce the variability of available bandwidth estimates, these cannot resolve systematic deviations such as the underestimation bias in case of random cross traffic and multiple tight links. The limitations of the state-of-the-art methods motivate us to explore the use of machine learning in end-to-end active and passive available bandwidth estimation. We investigate how to benefit from machine learning while using standard packet train probes for active available bandwidth estimation. To reduce the amount of required training data, we propose a regression-based scale- invariant method that is applicable without prior calibration to networks of arbitrary capacity. To reduce the amount of probe traffic further, we implement a neural network that acts as a recommender and can effectively select the probe rates that reduce the estimation error most quickly. We also evaluate our method with other regression-based supervised machine learning techniques. Furthermore, we propose two different multi-class classification-based meth- ods for available bandwidth estimation. The first method employs reinforcement learning that learns through the network path’s observations without having a training phase. We formulate the available bandwidth estimation as a single-state Markov Decision Process (MDP) multi-armed bandit problem and implement the ε-greedy algorithm to find the available bandwidth, where ε is a parameter that controls the exploration vs. exploitation trade-off. We propose another supervised learning-based classification method to ob- tain reliable available bandwidth estimates with a reduced amount of network overhead in networks, where available bandwidth changes very frequently. In such networks, reinforcement learning-based method may take longer to con- verge as it has no training phase and learns in an online manner. We also evaluate our method with different classification-based supervised machine learning techniques. Furthermore, considering the correlated changes in a network’s traffic through time, we apply filtering techniques on the estimation results in order to track the available bandwidth changes. Active probing techniques provide flexibility in designing the input struc- ture. In contrast, the vast majority of Internet traffic is Transmission Control Protocol (TCP) flows that exhibit a rather chaotic traffic pattern. We investigate how the theory of active probing can be used to extract relevant information from passive TCP measurements. We extend our method to perform the estima- tion using only sender-side measurements of TCP data and acknowledgment packets. However, non-fluid cross traffic, multiple tight links, and packet loss in the reverse path may alter the spacing of acknowledgments and hence in- crease the measurement noise. To obtain reliable available bandwidth estimates from noise-afflicted acknowledgment gaps we propose a neural network-based method. We conduct a comprehensive measurement study in a controlled network testbed at Leibniz University Hannover. We evaluate our proposed methods under a variety of notoriously difficult network conditions that have not been included in the training such as randomly generated networks with multiple tight links, heavy cross traffic burstiness, delays, and packet loss. Our testing results reveal that our proposed machine learning-based techniques are able to identify the available bandwidth with high precision from active and passive measurements. Furthermore, our reinforcement learning-based method without any training phase shows accurate and fast convergence to available band- width estimates