369 research outputs found

    Load Balancing via Random Local Search in Closed and Open systems

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    In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch server independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.Comment: Accepted to Sigmetrics 201

    Flow-level models for multipath routing

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    In this paper we study coordinated multipath routing at the flow-level in networks with routes of length one. As a first step the static case is considered, in which the number of flows is fixed. A clustering pattern in the rate allocation is identified, and we describe a finite algorithm to find this rate allocation and the clustering explicitly. Then we consider the dynamic model, in which there are stochastic arrivals and departures; we do so for models with both streaming and elastic traffic, and where a peak-rate is imposed on the elastic flows (to be thought of as an access rate). Lacking explicit expressions for the equilibrium distribution of the Markov process under consideration, we study its fluid and diffusion limits; in particular, we prove uniqueness of the equilibrium point. We demonstrate through a specific example how the diffusion limit can be identified; it also reveals structural results about the clustering pattern when the minimal rate is very small and the network grows large

    The Regulation of Motor Carriers in Pennsylvania

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    The Regulation of Motor Carriers in Pennsylvania

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    Exploring the relation between flapping behaviour and accelerometer signal during ascending flight, and a new approach to calibration

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    We understand little about the energetic costs of flight in free-ranging birds; in part since current techniques for estimating flight energetics in the wild are limited. Accelerometry is known to estimate energy expenditure through body movement in terrestrial animals, once calibrated using a treadmill with chamber respirometry. The flight equivalent, a wind tunnel with mask respirometry, is particularly difficult to instigate, and has not been applied to calibrate accelerometry. We take the first steps in exploring a novel method for calibrating accelerometers with flight energy expenditure. We collected accelerometry data for Harris’s Hawks Parabuteo unicinctus flying to varying heights up to 4.1 m over a small horizontal distance; the mechanical energy expended to gain height can be estimated from physical first principles. The relationship between accelerometry and mechanical energy expenditure was strong, and while a simple wing flapping model confirmed that accelerometry is sensitive to both changes in wing beat amplitude and frequency, the relationship was explained predominately by changes in wing beat frequency, and less so by changes in amplitude. Our study provides initial, positive evidence that accelerometry can be calibrated with body power using climbing flights, potentially providing a basis for estimating flapping flight metabolic rate at least in situations of altitude gain
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