Quality-Aware Massive Content Delivery in Digital Twin-Enabled Edge Networks

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication.However,various multimedia applications possess huge differences in terms of object oriented(i.e.,machine or user)and corresponding quality evaluation metric,which will significantly impact the design of encoding or decoding within content delivery strategy.To get over this dilemma,we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision(QoD)or Quality-of-Experience(QoE)for the guidance of content delivery.Then,in terms of machinecentric communication,a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment,which can achieve a balance among decision-making,delivered content,and encoding latency.Finally,in terms of user-centric communication,by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams,we develop a QoE-driven video enhancement scheme to supply high data fidelity.Numerical results demonstrate the remarkable performance improvement of massive content delivery.

Coded Multicasting for Content Delivery over Predictable Time-Varying Satellite Communication Networks

With the development of astronautic technology, communication satellites now have a tremendous gain in both quantity and quality, and have already shown their capability on multi-functional converged communication other than telecommunication. Under this circumstance, increasing the transmission efficiency of satellite communication network becomes a top priority. In this paper, we focus on content delivery service on satellite networks, where each ground station may have prefetched some file fragments. We cast this problem into a coded caching framework so as to exploit the coded multicast gain for minimizing the satellite communication load. We first propose an optimization-based coded multicast scheme by considering the special property that the satellite network topology is predictable and timevariant. Then, a greedy based fast algorithm is proposed, which can tremendously reduce the computation complexity with a small loss in optimality. Simulation experiments conducted on two Walker constellation satellite networks show that our proposed coded multicast method can efficiently reduce the communication load of satellite networks.

Algorithms to Calculate the Most Reliable Maximum Flow in Content Delivery Network

Calculating the most reliable maximum flow(MRMF)from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network(CDN).However,SDBA,as the current state-of-the-art MRMF algorithm,is too complex to meet real-time computing needs.This paper proposes a set of MRMF algorithms:NWCD(Negative Weight Community Deletion),SCPDAT(Single-Cycle Preference Deletion Approximation algorithm with Time constraint)and SCPDAP(Single-Cycle Preference Deletion Approximation algorithm with Probability constraint).NWCD draws on the“flow-shifting”algorithm of minimum cost and maximum flow,and further defines the concept of negative weight community.This algorithm continuously deletes the negative weight communities,which can increase reliability while keeping the flow constant in the residual graph.It is proven that when all negative weight communities are deleted,the corresponding maximum flow is the MRMF.SCPDAT tries to approach the optimal solution to the greatest extent possible within the limited time,while SCPDAP tries to reach the probability threshold in the shortest amount of time.Both of these adopt the strategy of first deleting single-cycle communities(which contribute more to the reliability with lower time cost).Experiments show that,compared with SDBA,NWCD combined with the probabilistic pruning achieves an order of magnitude improvement in time cost,while SCPDAT and SCPDAP demonstrate better time performance and increased applicability.

Cooperative Content Caching and Delivery in Vehicular Networks: A Deep Neural Network Approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network.As a promising alternative,cooperative content caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and limited caching capacities.In addition,the high mobility of vehicles renders the more complicated caching environment.Therefore,performing efficient cooperative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communication link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is formulated.Furthermore,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.

Students’Perceptions of Online Education During the COVID-19 Pandemic

The COVID-19 pandemic forced many universities around the world to move their educational activities onto online platforms.We conducted a survey in which asking undergraduates at a Chinese university how they felt about different aspects of online education during the pandemic.We received responses from 1,088 students.A majority of the students(67.9%)thought that physical classroom is better than online education and MOOCs.The students believed that teachers have improved their ability to teach online since the pandemic(67.3%)and online teaching is a suitable option in the current situation(65.8%).The students expressed satisfaction with the online educational resources and teachers’flexible use of online tools.However,the students felt that online education is stressful and affecting their health and social life.The pandemic has led to widespread use of online education,and we hope that online education can be better in the future.

云服务传递网络的多业务可信部署方法(英文)

The mutual-interference phenomenon among multiple applications delivered as services through Cloud Services Delivery Network(CSDN)influences their QoS seriously.In order to deploy multiple applications dependably and efficiently,we propose the Multiple Applications Co-Exist(MACE)method.MACE classifies multiple applications into different types and deploys them using isolation to some extent.Meanwhile,resource static allocation,dynamic supplement and resource reserved mechanism to minimize mutual-interference and maximize resource utilization are designed.After MACE is applied to a real large-scale CSDN and evaluated through 6-month measurement,we find that the CSDN load is more balanced,the bandwidth utilization increases by about 20%,the multiple applications'potential statistical multiplexing ratio decreases from 12% to 5%,and the number of complaint events affecting the dependability of CSDN services caused by multiple applications'mutual-interference has dropped to 0.Obviously,MACE offers a tradeoff and improvement for the dependability and efficiency goals of CSDN.

Users' Media Cloud Assisted D2D Communications for Distributed Caching Underlaying Cellular Network

In cellular networks, the proximity devices may share files directly without going through the e NBs, which is called Device-to-Device communications(D2D). It has been considered as a potential technological component for the next generation of communication. In this paper, we investigate a novel framework to distribute video files from some other proximity devices through users' media cloud assisted D2 D communication. The main contributions of this work lie in: 1) Providing an efficient algorithm Media Cloud Cluster Selecting Scheme(MCCSS) to achieve the reasonable cluster; 2) Distributing the optimum updating files to the cluster heads, in order to minimize the expected D2 D communication transmission hop for files; 3) Proposing a minimum the hop method, which can ensure the user obtain required file as soon as possible. Extensive simulation results have demonstrated the efficiency of the proposed scheme.

Edge Video CDN： A Wi-Fi Content Hotspot Solution

The emergence of smart edge-network content item hotspots, which are equipped with huge storage space （e.g., several GBs）, opens up the opportunity to study the possibility of delivering videos at the edge network. Different from both the conventional content item delivery network （CDN） and the peer-to-peer （P2P） scheme, this new delivery paradigm, namely edge video CDN, requires up to millions of edge hotspots located at users＇ homes/offices to be coordinately managed to serve mobile video content item. Specifically, two challenges are involved in building edge video CDN, including how edge content item hotspots should be organized to serve users, and how content items should be replicated to them at different locations to serve users. To address these challenges, we propose our data-driven design as follows. First, we formulate an edge region partition problem to jointly maximize the quality experienced by users and minimize the replication cost, which is NP-hard in nature, and we design a Voronoi-like partition algorithm to generate optimal service cells. Second, to replicate content items to edge-network content item hotspots, we propose an edge request prediction based replication strategy, which carries out the replication in a server peak offioading manner. We implement our design and use trace-driven experiments to verify its effectiveness. Compared with conventional centralized CDN and popularity-based replication, our design can significantly improve users＇ quality of experience, in terms of users＇ perceived bandwidth and latency, up to 40%.

Modeling in-network caching and bandwidth sharing performance in information-centric networking

Information-centric networking （ICN） proposes a content-centric paradigm which has some attractive advantages, such as network load reduction, low dissemination latency, and energy efficiency. In this paper, based on the analytical model of ICN with receiver-driven transport protocol employing least-recently used （LRU） replacement policy, we derive expressions to compute the average content delivery time of the requests＇ arrival sequence of a single cache, and then we extend the expressions to a cascade of caches＇ scenario. From the expressions, we know the quantitative relationship among the delivery time, cache size and bandwidth. Our results, analyzing the trade-offs between performance and resources in ICN, can be used as a guide to design ICN and to evaluation its performance.

PROSE: Proactive, Selective CDN Participation for P2P Streaming

Many production peer-to-peer （P2P） streaming systems use content delivery networks （CDN） to protect the user＇s quality of experiences. Thus, how to efficiently utilize the capacity of CDN （e.g., which peers receive services from the CDN nodes） is a problem of practical significance. Existing solutions adopt a passive, on-demand approach, which is inefficient in utilizing CDN resources. In this paper, we propose PROSE, a simple, novel scheme to achieve proactive, selective CDN participation for P2P streaming. PROSE introduces novel concepts such as choke point expansion nodes/super nodes and leads to efficient, light-weighted, and distributed algorithms to identify and serve these nodes using CDN. Our experimental results show that PROSE achieves at least 10%~25% performance improvement and 2~4 times overhead reduction compared with existing general CDN-P2P-hybrid schemes.