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1. An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning

   https://doi.org/10.1109/ICCC.2018.00010  

   Bura, Harshitha ; Lin, Nathan ; Kumar, Naveen ; Malekar, Sangram ; Nagaraj, Sushma ; Liu, Kaikai ( July 2018 , 2018 IEEE International Conference on Cognitive Computing (ICCC))

   The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems-including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number. 

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2. Multi-queue fair queueing

   Mohammad Hedayati, Kai Shen ( July 2019 , Proceedings of the 2019 Usenix Annual Technical Conference)

   Modern high-speed devices (e.g., network adapters, storage, accelerators) use new host interfaces, which expose multiple software queues directly to the device. These multi-queue interfaces allow mutually distrusting applications to access the device without any cross-core interaction, enabling throughput in the order of millions of IOP/s on multicore systems. Unfortunately, while independent device access is scalable, it also introduces a new problem: unfairness. Mechanisms that were used to provide fairness for older devices are no longer tenable in the wake of multi-queue design, and straightforward attempts to re-introduce it would require cross-core synchronization that undermines the scalability for which multiple queues were designed. To address these challenges, we present Multi-Queue Fair Queueing (MQFQ), the first fair, work-conserving scheduler suitable for multi-queue systems. Specifically, we (1) reformulate a classical fair queueing algorithm to accommodate multiqueue designs, and (2) describe a scalable implementation that bounds potential unfairness while minimizing synchronization overhead. Our implementation of MQFQ in Linux 4.15 demonstrates both fairness and high throughput. Evaluation with an NVMe over RDMA fabric (NVMf) device shows that MQFQ can reach up to 3.1 Million IOP/s on a single machine--20× higher than the state-of-the-art Linux Budget Fair Queueing. Compared to a system with no fairness, MQFQ reduces the slowdown caused by an antagonist from 3:78× to 1:33× for the FlashX workload and from 6:57× to 1:03× for the Aerospike workload (2× is considered "fair" slowdown). 

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3. Competitive Spatial Pricing for Urban Parking Systems: Network Structures and Asymmetric Information

   https://doi.org/10.1080/24725854.2021.1937755  

   Wu, Yuguang ; He, Qiao-Chu ; Wang, Xin ( August 2021 , IISE Transactions)

   null (Ed.)

   Inspired by new technologies to monitor parking occupancy and process market signals, we aim to expand the application of demand-responsive pricing in the parking industry. Based on a graphical Hotelling model wherein each garage has information for its incoming parking demand, we consider a general competitive spatial pricing in parking systems under an asymmetric information structure. We focus on the impact of urban network structure on the incentive of information sharing. Our analyses suggest that the garages are always better off in a circular-networked city, while they could be worse off in the suburbs of a star-networked city. Nevertheless, the overall revenue for garages is improved and the aggregate congestion is reduced under information sharing. Our results also suggest that information sharing helps garages further exploit the customers who in turn become worse-off. Therefore, policy-makers should carefully evaluate their transportation data policy since impacts on the service-providers and the customers are typically conflicting. Using the SFpark data, we empirically confirmed the value of information sharing. In particular, garages with higher price-demand elasticity and lower demand variance tend to enjoy larger benefits via information sharing. These insights support the joint design of parking rates structure and information systems. 

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4. A robust queueing network analyzer based on indices of dispersion

   https://doi.org/10.1002/nav.22010  

   Whitt, Ward ; You, Wei ( July 2021 , Naval Research Logistics (NRL))

   We develop a robust queueing network analyzer algorithm to approximate the steady‐state performance of a single‐class open queueing network of single‐server queues with Markovian routing. The algorithm allows nonrenewal external arrival processes, general service‐time distributions and customer feedback. The algorithm is based on a decomposition approximation, where each flow is partially characterized by its rate and a continuous function that measures the stochastic variability over time. This function is a scaled version of the variance‐time curve, called the index of dispersion for counts (IDC). The required IDC functions for the external arrival processes can be calculated from the model primitives or estimated from data. Approximations for the IDC functions of the internal flows are calculated by solving a set of linear equations. The theoretical basis is provided by heavy‐traffic limits for the flows established in our previous papers. A robust queueing technique is used to generate approximations of the mean steady‐state performance at each queue from the IDC of the total arrival flow and the service specification at that queue. The algorithm's effectiveness is supported by extensive simulation studies.

    

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5. Approximately Reversible Stochastic Processing Networks

   Shah, Devavrat ( July 2019 , INFORMS Applied Probability Society Conference)

   The property of (quasi-)reversibility of Markov chains have led to elegant characterization of steady-state distribution for complex queueing networks, e.g. celebrated Jackson networks, BCMP (Baskett, Chandi, Muntz, Palacois) and Kelly theorem. In a nutshell, despite the complicated interaction, in the steady-state, the queues in such networks exhibit independence and subsequently lead to explicit calculations of distributional properties of the queuing network that may seem impossible at the outset. The model of stochastic processing network (cf. Harrison 2000) captures variety of dynamic resource allocation problems including the flow-level networks used for modeling bandwidth sharing in the Internet, switched networks (cf. Shah, Wischik 2006) for modeling packet scheduling in the Internet router and wireless medium access, and hybrid flow-packet networks for modeling job-and-packet level scheduling in data centers. Unlike before, an appropriate resource allocation or scheduling policy is required in such networks to achieve good performance. Given the complexity, asymptotic analytic approaches such as fluid model or Lyapunov-Foster criteria to establish positive-recurrence and heavy traffic or diffusion approximation to characterize the scaled steady-state distribution became method of choice. A remarkable progress has been made along these lines over the past few decades, but there is a need for much more to match the explicit calculations in the context of reversible networks. In this work, we will present an alternative to this approach that leads to non-asymptotic, explicit characterization of steady-state distribution akin BCMP / Kelly theorems. This involves (a) identifying a "relaxation" of the given stochastic processing network in terms of an appropriate (quasi-)reversible queueing network, and (b) finding a resource allocation or scheduling policy of interest that "emulates" the "relaxed" networks within "small error". The proof is in the puddling -- we will present three examples of this program: (i) distributed scheduling in wireless network, (ii) scheduling in switched networks, and (iii) flow-packet scheduling in a data center. The notion of "baseline performance" (cf. Harrison, Mandayam, Shah, Yang 2014) will naturally emerges as a consequence of this program. We will discuss open questions pertaining multi-hop networks and computation complexity. 

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