812 research outputs found

    Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals

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    We consider a wireless network with a base station serving multiple traffic streams to different destinations. Packets from each stream arrive to the base station according to a stochastic process and are enqueued in a separate (per stream) queue. The queueing discipline controls which packet within each queue is available for transmission. The base station decides, at every time t, which stream to serve to the corresponding destination. The goal of scheduling decisions is to keep the information at the destinations fresh. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we derive a lower bound on the AoI performance achievable by any given network operating under any queueing discipline. Then, we consider three common queueing disciplines and develop both an Optimal Stationary Randomized policy and a Max-Weight policy under each discipline. Our approach allows us to evaluate the combined impact of the stochastic arrivals, queueing discipline and scheduling policy on AoI. We evaluate the AoI performance both analytically and using simulations. Numerical results show that the performance of the Max-Weight policy is close to the analytical lower bound

    Practical Algorithms for Multicast Support in Input Queues Switches

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    Abstract — This paper deals with multicast flow support in N × N Input Queued switch architectures. A practical approach to support multicast traffic is presented, assuming that O(N) queues are available at each input port. The focus is on dynamic queueing policies, where, at each input port, multicast flows are assigned to one among the available queues when flows become active: flows are assigned to queues according to switch queue status and, possibly, to flow information. We discuss queueing assignments, scheduling algorithms and flow activity definition models. We explain why dynamic queueing disciplines may outperform static policies, and we show that, even in the most favorable conditions for static policies, they provide comparable performance. I

    When Backpressure Meets Predictive Scheduling

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    Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within O(Ï”)O(\epsilon) of the optimal, for any Ï”>0\epsilon>0, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal [O(Ï”),O(log⁥(1/Ï”))][O(\epsilon), O(\log(1/\epsilon))] tradeoff for systems without prediction

    Task partitioning in insect societies. II. Use of queueing delay information in recruitment

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    The collection and handling of colony resources such as food, water, and nest-construction material is often divided into subtasks in which the material is passed from one worker to another. This is known as task partitioning. If task; are partitioned with direct transfer of material between foragers and receivers, queueing delays can occur as individuals search or wait for a transfer partner. Changes in environmental conditions and relative number of foragers and receivers affect these delays as well as colony ergonomic efficiency. These delays are used in recruitment in both honeybees and Polybia wasps. This study investigates the distribution of queueing delays and the information content and quality of those delays using a stochastic-simulation model. Information quality increases with colony size. When the relative proportions of foragers and receivers are suboptimal, the group in excess has better information. Individuals can increase information quality of delays by two mechanisms: averaging over consecutive trips and averaging over multiple transfers within a trip where direct transfer occurs. We suggest that multiple transfer occurs in the honeybee in order to improve information quality
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