CDN is a crucial Internet infrastructure ensuring quick access to Internet content. With the expansion of CDN scenarios, beyond delay, resource types like bandwidth and CPU are also important for CDN performance. Our measurements highlight the distinct impacts of various resource types on different CDN requests. Unfortunately, mainstream CDN server selection schemes only consider a single resource type and are unable to choose the most suitable servers when faced with diverse resource types. To fill this gap, we propose Polygon, a QUIC-powered CDN server selection system that is aware of multiple resource demands. Being an advanced transport layer protocol, QUIC equips Polygon with customizable transport parameters to enable the seamless handling of resource requirements in requests. Its 0-RTT and connection migration mechanisms are also utilized to minimize delays in connection and forwarding. A set of collaborative measurement probes and dispatchers are designed to support Polygon, being responsible for capturing various resource information and forwarding requests to suitable CDN servers. Real-world evaluations on the Google Cloud Platform and extensive simulations demonstrate Polygon’s ability to enhance QoE and optimize resource utilization. The results show up to a 54.8% reduction in job completion time, and resource utilization improvements of 13% in bandwidth and 7% in CPU.

Online Social Networks (OSNs) play a significant role in people's daily life. Increasing OSN traffic promotes the requirement for building self-operated Content Delivery Networks (CDNs) to deliver OSN media data efficiently and reduce traffic costs. OSN data in CDNs is heavily influenced by social connectivity, such as friendships. To reduce CDN network traffic by better using social connectivity information, we propose SocialCache, a pervasive social-aware caching strategy in self-operated CDNs. SocialCache supports several social connectivity metric options that reflect the importance of the OSN users and the popularity of the media files. Through the improvement of the cache replacement algorithm in the CDN node and the communication design between nodes, SocialCache realizes the optimization of network traffic. Worth mentioning, SocialCache can easily integrate into mainstream CDN architectures while protecting user privacy. We implement SocialCache on Mininet, using real-world network measurements for CDN hierarchy and a hill-climbing algorithm for parameter selection. SocialCache outperforms a range of state-of-the-art baselines on three real-world OSN datasets. On the Twitter dataset, SocialCache reduces the network traffic volume by 6.40% and improves the cache hit ratio by 14.22%.

Today, Internet service deployment is typically implemented with server replication at multiple locations. Domain name system (DNS), which translates human-readable domain names into network-routable IP addresses, is typically used for distributing users to different server replicas. However, DNS relies on several network-based queries and the queries delay the connection setup process between the client and the server replica. In this article, we propose Artemis, a practical low-latency naming and routing system that supports optimal server (replica) selection based on user-defined policies and provides lower query latencies than DNS. Artemis uses a DNS-like domain name-IP mapping for replica selection and achieves low query latency by combining the name resolution process with the transport layer handshake process. In Artemis, all server replicas at different locations share the same anycast IP address, called Service Address. Clients use the Service Address to establish a transport layer connection with the server. The client's initial handshake packet is routed over an overlay network to reach the optimal server. Then the server migrates the transport layer connection to its original unicast IP address after finishing the handshake process. After that, service discovery is completed, and the client communicates with the server directly via IP addresses. To validate the effectiveness of Artemis, we evaluate its performance via both real trace-driven simulation and real-world deployment. The result shows that Artemis can handle a large number of connections and reduce the connection setup latency compared with state-of-the-art solutions. More specifically, our deployment across 11 Google data centers shows that Artemis reduces the connection setup latency by 39.4% compared with DNS.

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks.

The presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly important contexts of visual surveillance and vehicle autonomy. A key part of this problem is robust raindrop detection such that the potential for performance degradation in effected image regions can be identified. Here we address the problem of raindrop detection in colour video imagery by considering three varying region proposal approaches with secondary classification via a number of novel convolutional neural network architecture variants. This is verified over an extensive dataset with in-frame raindrop annotation to achieve maximal 0.95 detection accuracy with minimal false positives compared to prior work. Our approach is evaluated under a range of environmental conditions typical of all-weather automotive visual sensing applications.