The provision of performance-aware video conferencing services today relies on approaches that focus on data compression and client-side bitrate adaptation techniques to optimize transmission. However, these methods fail to quickly respond to fluctuations in network conditions, thereby compromising the quality of service for transmissions. For this reason, this article aims to propose a novel traffic scheduling-based video transmission optimization solution from the perspective of the network service provider. We first investigate the resource requirements of video conferences and present the experiential performance of video conferences under different network conditions and network competition. Based on these results, we design a service-customized routing mechanism called VCApather that minimizes network contention. We then provide implementation solutions for the control plane and the data plane of VCApather . We evaluate VCApather using a fully meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather could satisfy 69.8% more QoE requirements and yielded an average bitrate improvement of 1.74 \(\times\) .

Real-time conferencing applications (RCA) are crucial for online learning and e-commerce. However, they can be affected by network fluctuations because they are heavily dependent on cloud network connections. However, there is a dearth of systematic studies that aim to pinpoint the specific network links where these fluctuations occur. We introduce a contrastive learning approach for locating anomalies, based on actual traffic from real-time conferencing applications. This method is trained on unlabeled data, which means that it does not require the creation of a large-scale training dataset. The results illustrate the robust localization ability, achieving an accuracy rate of more than 95%, demonstrating its adaptability to commonly used real-time conferencing applications.

The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, image, and video content. Beyond digital content creation, GAI’s capability to analyze complex data distributions holds immense potential for next-generation networks and communications, especially given the swift rise of video conferencing applications (VCAs). This paper presents a dynamic, real-time method for detecting anomalous network links in video conferencing applications. The proposed tool, MonkeyGPT, generates tracing representations of network activity and trains a large language model from scratch to serve as a detection system based on network traffic data. Unlike traditional methods, MonkeyGPT provides an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a wider range of anomalies. We demonstrate the effectiveness of MonkeyGPT as an anomaly detection tool in real-world VCAs. The results indicate that the model possesses strong detection capabilities, achieving an accuracy rate of over 97%. It is applicable to various platforms, including Zoom, Microsoft Teams, Tencent Meeting, and Feishu, showcasing its robust adaptability.

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×.

With the development of cross-datacenter services, accurate and low-cost network performance measurement enables better traffic scheduling. However, the existing network measurement suffers from a lack of telemetry granularities and excessive costs. Besides, the implementation of INT in the WAN remains undefined. In this work, We propose WAN-INT, a two-phase path orchestration algorithm designed to address the challenges posed by limited communication resources and varying link states in WAN scenarios. By generating a telemetry policy based on this algorithm, it can effectively adapt to the requirements of various applications and network states, achieving a balance between telemetry quality and cost limitations. We conduct experiments in a commercial WAN environment. Results show that WAN-INT outperforms existing schemes and reaches a good compromise between the telemetry quality and cost constraints. Compared with the state-of-the-art telemetry system, WAN-INT effectively reduces by at least 43% of telemetry cost while ensuring telemetry quality.

Edge computing architectures can help us quickly process the data collected by Internet of Things (IoT) and caching files to edge nodes can speed up the response speed of IoT devices requesting files. Blockchain architectures can help us ensure the security of data transmitted by IoT. Therefore, we have proposed a system that combines IoT devices, edge nodes, remote cloud, and blockchain. In the system, we designed a new algorithm in which blockchain-assisted compressed algorithm of federated learning is applied for content caching, called CREAT to predict cached files. In the CREAT algorithm, each edge node uses local data to train a model and then uses the model to learn the features of users and files, so as to predict popular files to improve the cache hit rate. In order to ensure the security of edge nodes’ data, we use federated learning (FL) to enable multiple edge nodes to cooperate in training without sharing data. In addition, for the purpose of reducing communication load in FL, we will compress gradients uploaded by edge nodes to reduce the time required for communication. What is more, in order to ensure the security of the data transmitted in the CREAT algorithm, we have incorporated blockchain technology in the algorithm. We design four smart contracts for decentralized entities to record and verify the transactions to ensure the security of data. We used MovieLens data sets for experiments and we can see that CREAT greatly improves the cache hit rate and reduces the time required to upload data.

The smart construction site is developing rapidly with the intelligentization of industrial management. Intelligent devices are being widely deployed in construction industry to support artificial intelligence applications. Video surveillance is a core function of smart construction, which demands both high accuracy and low latency. The challenge is that the computation and networking resources in a construction site are often limited, and the inefficient scheduling policies create congestions in the network and bring additional delay that is unbearable to realtime surveillance. Adaptive video configuration and edge computing have been proposed to improve accuracy and reduce latency with limited resources. However, optimizing the video configuration and task scheduling in edge computing involves several factors that often interfere with each other, which significantly decreases the performance of video surveillance. In this article, we present an edge-based solution of video surveillance in the smart construction site assisted by a graph neural network. It leverages the distributed computing model to realize flexible allocation of resources. A graph-assisted hierarchical reinforcement learning algorithm is developed to illustrate the feature of the mobile-edge network and optimize the scheduling policy by the Deep-$Q$Network. We implement and test the proposed solution in the commercial residential buildings of a fortune global 500 real estate company and observe that the proposed algorithm is efficient to maintain a reliable accuracy and keep lower delay. We further conduct a case study to demonstrate the superiority of the proposed solution by comparing it with traditional mechanisms.

The Internet of Medical Things is developing rapidly in recent years. However, the timeliness and security of healthcare applications challenge its adoption. In this article, we propose a blockchain-assisted edge computing platform that timely and securely processes time-sensitive healthcare applications. We propose a blockchain-assisted framework that leverages distributed edge servers to achieve fast data processing. We design smart contracts to verify the identity and data credibility of network entities. We formulate the problem as a directed acyclic graph organized scheduling model and develop online orchestrating algorithms to meet the timeliness requirement. We implement the blockchain prototype and evaluate the performance of the proposed algorithm under extensive configurations. Results show that fast blockchain-assisted edge computing platform for healthcare achieves a significant timeliness guarantee, and at the same time outperforms conventional schemes from the latency perspective.

Edge computing is promising to solve the latency issue in the Internet of Vehicles (IoV). However, due to decentralization, traditional edge computing suffers in management, deployment, and security. Containerization relaxes resource deployment and migration problems, but current container scheduling policies are inefficient to process complicated tasks based on directed acyclic graph or DAG structures. In this article, we design a containerized edge computing platform CUTE, which provides low-latency computation services for the Internet of Vehicles. The centralized controller is empowered with resource management and orchestration, and containers are scheduled to appropriate edge servers to optimize the computation delay. CUTE is also integrated with blockchain to improve network security. We formulate the vehicle task offloading and container scheduling problems and develop a heuristic container scheduling algorithm for DAG-based computation tasks submitted by vehicles remotely. We implement and deploy CUTE into the China Mobile Network, and conduct comprehensive experiments and a case study. The experiment results show that CUTE can provide low-latency computation services for vehicular applications and that the heuristic algorithm outperforms traditional container scheduling policies.

Edge computing, especially multiaccess edge computing, is seen as a promising technology to improve the Quality of user Experience (QoE) of many artificial intelligence (AI) applications in the evolution toward Internet-of-Things (IoT) infrastructure. However, the management and deployment of massive edge data centers bring new challenges for the current network. In this article, we propose a new edge-based IoT platform for AI (EBI-PAI), based on software-defined network (SDN) and serverless technology. EBI-PAI provides a unified service calling interface and schedules the resources automatically to satisfy the QoE requirements of users. To optimize performances during incremental deployment, we formulate the deployment problem, prove its complexity, and design heuristic algorithms to solve it. We implement EBI-PAI based on an opensource serverless project and deploy it in real networks. To evaluate EBI-PAI, we conduct comprehensive simulations based on the generated and real-world network topology, and real-world base station data set. The simulation results show that EBI-PAI can greatly improve QoE with the same budget and save the budget to achieve similar QoE. We finally carry out a case study with real user demands, and it further validates the simulation results.

User data allowance trading emerges as a promising practice in mobile data networks since it can help mobile networks to attract more users. However, to date, there is no study on user data allowance trading in mobile networks. In this paper, we develop a truthful framework that allows users to bid for data allowance. We focus on preventing price cheating, guaranteeing fairness, and minimizing trading maintenance cost in trading. We formulate the data trading process as a double auction problem and develop algorithms to solve the problem. In particular, we use a uniform price auction based on a competitive equilibrium to defend against price cheating and provide fair-ness. Meanwhile, we leverage linear programming to minimize trading maintenance cost. We conduct extensive simulations to demonstrate the performance of the proposed mechanism. The simulation results show that our trading mechanism is truthful and fair, while incurring a minimized maintenance cost.

User data allowance trading is emerging as a promising field in mobile data networks. Mobile operators are establishing data trading platforms to attract more users. To date, there has been no coherent study on user data allowance trading. In this paper, we develop a truthful framework that allows users to bid for data allowance. We focus on preventing price cheating, guaranteeing fairness and minimizing trading maintenance cost. We model the data trading process as a double auction problem. We develop algorithms to solve the problem. The algorithms use a uniform price based on a competitive equilibrium to defend against price cheating and provide fairness, and use linear programming to minimize trading maintenance cost. We conduct extensive simulations to testify the proposed mechanism. Results show that our mechanism is truthful, fair and can minimize the cost of trading.

Information-Centric Networking (ICN) provides substantial flexibility for users. One of the most important features of ICN is the universal in-network caching. The characteristics of ICN make it substantially different from traditional caching systems. In this paper we propose an age-based cooperative caching scheme in response to the special characteristics of ICN. We leverage the coupling between routing and caching in ICN to develop a light-weight collaboration mechanism that adaptively pushes popular contents to the network edge. We evaluate our approach using real traces and realistic network topology. Results show that our approach can significantly reduce network delay and traffic, and outperforms existing schemes.

The goal of this paper is to improve wireless AP caching by leveraging in-network caching. We observe that by treating routers as an in-network storage extension, we can relieve the storage limitation of APs. The unique challenge is that APs and routers cannot have a full collaboration, which makes the problem different from traditional cooperative caching problems. We study how APs can optimize caching decisions by using in-network caching information without controlling routers.

The Internet is a great hit in human history. However, it has evolved greatly from its original incarnation. Content distribution is playing a central role in today's Internet, which makes it difficult for the conventional host-to-host communication to meet the ever-increasing demands. In this paper, we present a novel “service and information oriented network architecture” (SIONA). The key aspect of SIONA is the name-based two-dimensional routing paradigm that provides scalable routing, caching and content delivery. We argue that SIONA solves the problems of mobile Internet by naturally supporting mobility, and provides network layer P2P for massive data distribution. Evaluation is conducted to investigate its caching and mobility performance.