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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Datacenter at the Airport: Reasoning about Time-Dependent Parking Lot Occupancy
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Datacenter at the Airport: Reasoning about Time-Dependent Parking Lot Occupancy

机译：机场的数据中心：关于时空停车场占用的推理

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摘要

Recently, Olariu et al. [3], [7], [18], [19], [20] proposed to refer to a dynamic group of vehicles whose excess computing, sensing, communication, and storage resources can be coordinated and dynamically allocated to authorized users, as a vehicular cloud. One of the characteristics that distinguishes vehicular clouds from conventional clouds is the dynamically changing amount of available resources that, in some cases, may fluctuate rather abruptly. In this work, we envision a vehicular cloud involving cars in the long-term parking lot of a typical international airport. The patrons of such a parking lot are typically on travel for several days, providing a pool of cars that can serve as the basis for a datacenter at the airport. We anticipate a park and plug scenario where the cars that participate in the vehicular cloud are plugged into a standard power outlet and are provided Ethernet connection to a central server at the airport. In order to be able to schedule resources and to assign computational tasks to the various cars in the vehicular cloud, a fundamental prerequisite is to have an accurate picture of the number of vehicles that are expected to be present in the parking lot as a function of time. What makes the problem difficult is the time-varying nature of the arrival and departure rates. In this work, we concern ourselves with predicting the parking occupancy given time-varying arrival and departure rates. Our main contribution is to provide closed forms for the probability distribution of the parking lot occupancy as a function of time, for the expected number of cars in the parking lot and its variance, and for the limiting behavior of these parameters as time increases. In addition to analytical results, we have obtained a series of empirical results that confirm the accuracy of our analytical predictions.
机译：最近，Olariu等。 [3]，[7]，[18]，[19]，[20]建议引用动态车辆组，其多余的计算，传感，通信和存储资源可以协调并动态分配给授权用户，例如车辆云。车辆云与传统云的区别之一是可用资源的动态变化，在某些情况下，可用资源可能会突然波动。在这项工作中，我们设想了一个典型的国际机场的长期停车场中涉及汽车的车辆云。这样的停车场的顾客通常要出差几天，提供可以作为机场数据中心基础的汽车池。我们预计会出现停放即插即用的情况，将参与车辆云计算的汽车插入标准电源插座，并提供与机场中央服务器的以太网连接。为了能够调度资源并为车辆云中的各种汽车分配计算任务，一个基本前提是要准确地了解预计停车场中将存在的车辆数量随时间变化的函数。时间。使问题变得困难的是到达和离开速度的时变性质。在这项工作中，我们会根据给定的随时间变化的到达和离开速度来预测停车位。我们的主要贡献是提供封闭形式的停车位占用率随时间变化的函数，停车位中车辆的预期数量及其方差以及这些参数随时间增加的限制行为。除分析结果外，我们还获得了一系列经验结果，这些结果证实了我们的分析预测的准确性。

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