The Domain Name System (DNS) serves as a foundational networking service, yet its inherent time-to-live (TTL)-based cache mechanism presents a conundrum—striving for both low query latency and robust cache consistency proves challenging. To address this, we introduce Aeacus: a middleware seamlessly integrated into the 5G core, engineered to optimize name resolution for QUIC. Aeacus adeptly fortifies DNS with substantial cache consistency by capitalizing on QUIC handshake states to detect cache inconsistency, without compromising query delay. Furthermore, Aeacus orchestrates the amalgamation of DNS queries and QUIC handshake messages, effectively truncating one round-trip of message exchange and reviving expired DNS cache to bolster cache hit rates. Our dual-pronged deployment, encompassing both commercial and test 5G networks, demonstrates Aeacus’ prowess. In direct comparison with DNS, Aeacus successfully truncates connection setup delays by a remarkable 8.9% to 71.8%, all while introducing a mere 5.9% overhead attributed to supplementary packet processing and forwarding expenses. Importantly, existing DNS-based systems reap the benefits of Aeacus without necessitating modifications. We demonstrate Aeacus’ seamless enhancement of DNS-based load balancers, extending QUIC's 0-RTT handshake to include 0-RTT connection setup and service migration.

Deep Reinforcement Learning (DRL) has rapidly gained traction as a viable method for optimizing control in real-time video streaming. Recent research endeavors are shifting towards enabling direct DRL control over multiple streaming parameters, moving away from traditional bitrate only control. Despite this growing interest, there is a notable absence of a dedicated open-source framework to facilitate such research.

Real-time deep video analytic at the edge is an enabling technology for emerging applications, such as vulnerable road user detection for autonomous driving, which requires highly accurate results of model inference within a low latency. In this paper, we investigate the accuracy-latency trade-off in the design and implementation of real-time deep video analytic at the edge. Without loss of generality, we select the widely used YOLO-based object detection and WebRTC-based video streaming for case study. Here, the latency consists of both networking latency caused by video streaming and the processing latency for video encoding/decoding and model inference. We conduct extensive measurements to figure out how the dynamically changing settings of video streaming affect the achieved latency, the quality of video, and further the accuracy of model inference. Based on the findings, we propose a mechanism for adapting video streaming settings (i.e. bitrate, resolution) online to optimize the accuracy of video analytic within latency constraints. The mechanism has proved, through a simulated setup, to be efficient in searching the optimal settings.

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.

National roaming, multi-SIM and edge computing constitute key 5G technologies for the cooperative perception and remote driving of L4 (automated) vehicles. To that end, this article reports our progress to trial these technologies at the multi-PLMN experimental 5G SA testbed of Aalto University, Finland. Overall, the objective is to qualify 5G as a core connectivity for connected, cooperative and automated mobility.

Today, Internet service deployment is typically implemented with server replication at multiple locations for the purpose of load balancing, failure tolerance, and user experience optimization. Domain name system (DNS) is responsible for translating human-readable domain names into network-routable IP addresses. When multiple replicas exist, upon the arrival of a query, DNS selects one replica and responds with its IP address. Thus, the delay caused by the process of DNS query including the selection of replica is part of the connection setup latency.

Multitask learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve the generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous features for each task. To address this problem, we first construct an integrated graph of a set of bipartite graphs to build a connection among different tasks. We then propose a non-negative matrix factorization-based multitask method (MTNMF) to learn a common semantic feature space underlying different heterogeneous feature spaces of each task. Moreover, an improved version of MTNMF (IMTNMF) is proposed, in which we do not need to construct the correlation matrix between input features and class labels, avoiding the information loss. Finally, based on the common semantic features and original heterogeneous features, we model the heterogenous MTL problem as a multitask multiview learning (MTMVL) problem. In this way, a number of existing MTMVL methods can be applied to solve the problem effectively. Extensive experiments on three real-world problems demonstrate the effectiveness of our proposed methods, and the improved version IMTNMF can gain about 2% average accuracy improvement compared with MTNMF.