Knowing how individuals move between places is fundamental to advance our understanding of human mobility (González et al. 2008 Nature 453, 779-782. (doi:10.1038/nature06958)), improve our urban infrastructure (Prato 2009 J. Choice Model. 2, 65 -100. (doi:10.1016/S1755-5345(13) 70005-8)) and drive the development of transportation systems. Current route-choice models that are used in transportation planning are based on the widely accepted assumption that people follow the minimum cost path (Wardrop 1952 Proc. Inst. Civ. Eng. 1, 325-362. (doi:10.1680/ipeds.1952), despite little empirical support. Fine-grained location traces collected by smart devices give us today an unprecedented opportunity to learn how citizens organize their travel plans into a set of routes, and how similar behaviour patterns emerge among distinct individual choices. Here we study 92 419 anonymized GPS trajectories describing the movement of personal cars over an 18-month period. We group user trips by origin-destination and we find that most drivers use a small number of routes for their routine journeys, and tend to have a preferred route for frequent trips. In contrast to the cost minimization assumption, we also find that a significant fraction of drivers' routes are not optimal. We present a spatial probability distribution that bounds the route selection space within an ellipse, having the origin and the destination as focal points, characterized by high eccentricity independent of the scale. While individual routing choices are not captured by path optimization, their spatial bounds are similar, even for trips performed by distinct individuals and at various scales. These basic discoveries can inform realistic route-choice models that are not based on optimization, having an impact on several applications, such as infrastructure planning, routing recommendation systems and new mobility solutions.

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The need for efficient counter architecture has arisen for the following two reasons. Firstly, a number of data streaming algorithms and network management applications require a large number of counters in order to identify important traffic characteristics. And secondly, at high speeds, current memory devices have significant limitations in terms of speed (DRAM) and size (SRAM). For some applications no information on counters is needed on a per-packet basis and several methods have been proposed to handle this problem with low SRAM memory requirements. However, for a number of applications it is essential to have the counter information on every packet arrival. In this paper we propose two, computationally and memory efficient, randomized algorithms for approximating the counter values. We prove that proposed estimators are unbiased and give variance bounds. A case study on Multistage Filters (MSF) over the real Internet traces shows a significant improvement by using the active counters architecture.Index Terms-Counter architecture, high-speed measurements, router, data streaming algorithms.

Abstract-In recent years we have witnessed a great interest in large distributed computing platforms, also known as clouds. While these systems offer enormous computing power, they are major energy consumers. In existing data centers CPUs are responsible for approximately half of the energy consumed by the servers. A promising technique for saving CPU energy consumption is dynamic speed scaling, in which the speed at which the processor is run is adjusted based on demand and performance constraints. In this paper we look at the problem of allocating the demand in the network of processors (each being capable to perform dynamic speed scaling) to minimize the global energy consumption/cost subject to a performance constraint. The nonlinear dependence between the energy consumption and the performance as well as the high variability in the energy prices result in a nontrivial resource allocation. The problem can be abstracted as a fully distributed convex optimization with a linear constraint. On the theoretical side, we propose two low-overhead fully decentralized algorithms for solving the problem of interest and provide closed-form conditions that ensure stability of the algorithms. Then we evaluate the efficacy of the optimal solution using simulations driven by the real-world energy prices. Our findings indicate a possible cost reduction of 10 − 40% compared to power-oblivious 1/N load balancing, for a wide range of load factors. Index Terms-

In this paper we address the challenge of inferring the road network of a city from crowd-sourced GPS traces. While the problem has been addressed before, our solution has the following unique characteristics: (i) we formulate the road network inference problem as a network alignment optimization problem where both the nodes and edges of the network have to be inferred, (ii) we propose both an offline (Kharita) and an online (Kharita * ) algorithm which are intuitive and capture the key aspects of the optimization formulation but are scalable and accurate. The Kharita * in particular is, to the best of our knowledge, the first known online algorithm for map inference, (iii) we test our approach on two real data sets and both our code and data sets have been made available for research reproducibility.