Abstract. Design of distributed architectures for content-based publishsubscribe (pub-sub) service networks has been a challenging problem. To best support the highly dynamic and diversified content-based pub-sub communication, we propose a new architectural design called MEDYM -Match-Early with DYnamic Multicast. MEDYM follows the End-to-End distributed system design principle. It decouples a pub-sub service into two functionalities: complex, application-specific matching at network edge, and simple, generic multicast routing in the network. This architecture achieves low computation cost in event matching and high network efficiency and flexibility in event routing. For higher scalability, we describe a novel approach to extend MEDYM to a hierarchy structure called H-MEDYM, which effectively balances the trade-off between event delivery efficiency and server states maintenance. We evaluate MEDYM and H-MEDYM using detailed simulations and real-world experiments, and compare them with major existing design approaches. Results show that MEDYM and H-MEDYM achieve high event delivery efficiency and system scalability, and their advantages are most prominent when user subscriptions are highly selective and diversified.
Abstract-Efficient event delivery in a content-based publish/subscribe system has been a challenging problem. Existing group communication solutions, such as IP multicast or application-level multicast techniques, are not readily applicable due to the highly heterogeneous communication pattern in such systems. We first explore the design space of event routing strategies for content-based publish/subscribe systems. Two major existing approaches are studied: filter-based approach, which performs content-based filtering on intermediate routing servers to dynamically guide routing decisions, and multicastbased approach, which delivers events through a few high-quality multicast groups that are pre-constructed to approximately match user interests. These approaches have different trade-offs in the routing quality achieved and the implementation cost and system load generated. We then present a new routing scheme called Kyra that carefully balance these trade-offs. Kyra combines the advantages of content-based filtering and eventspace partitioning in the existing approaches to achieve better overall routing efficiency. We use detailed simulations to evaluate Kyra and compare it with existing approaches. The results demonstrate the effectiveness of Kyra in achieving high network efficiency, reducing implementation cost and balancing system load across the publish-subscribe service network.
The classification and detection of traffic status plays a vital role in the urban smart transportation system. The classification and mastery of the traffic status at different time periods and sections will help the traffic management department to optimize road management and implement rescue in real time. Travelers can follow the traffic conditions. We choose the best route to effectively improve travel efficiency and safety. However, due to factors such as weather, time of day, lighting, and sample labeling costs, the existing classification methods are insufficient in real time and detection accuracy to meet application requirements. In order to solve this problem, this article aims to effectively transfer and apply the pretrained model learned on large-scale image data sets to small-sample road traffic data sets. By sharing common visual features, model weight parameter migration, and fine-tuning, the road is finally optimized. Traffic conditions classification is based on Traffic-Net. Experiments show that the method in this article can not only obtain a prediction accuracy of more than 96% but also can effectively reduce the model training time and meet the needs of practical applications.
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