Efficient Content Distribution Research Articles

A cost-efficient content distribution optimization model for fog-based content delivery networks

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

Digital Content Creation: An Analysis of the Impact of Recommendation Systems

The success of digital content platforms, such as YouTube, relies on both the creativity of independent content creators and the efficiency of content distribution. By sharing advertising revenue with content creators, these platforms can motivate creators to exert greater effort. Most platforms use recommendation systems to deliver personalized content recommendations to each consumer. As creators’ revenues are contingent on their demand, the demand allocation criteria inherent in the recommendation system can influence their content creation behavior. In this paper, we investigate the influence of a platform’s recommendation system on revenue-sharing plans, content creation, profits, and welfare. Our results show that a platform could benefit by biasing recommendations, that is, recommending content that is not an ideal match to a consumer’s preference, to incentivize creators to produce better-quality content. We refer to this as a biased recommendation strategy. Interestingly, we find that such a biased recommendation strategy may lead to a win-win in which the platform, consumers, and content creators can benefit. Our study also shows that consumers may be worse off when they are more knowledgeable and less dependent on the recommendation system. In addition, the platform, consumers, and creators can benefit when the platform has more accurate information on consumer preferences. This paper was accepted by Raphael Thomadsen, marketing. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03655 .

Resource allocation for content distribution in IoT edge cloud computing environments using deep reinforcement learning

This study presents a novel approach to optimize resource allocation, aiming to boost the efficiency of content distribution in Internet of Things (IoT) edge cloud computing environments. The proposed method termed the Caching-based Deep Q-Network (CbDQN) framework, dynamically allocates computational and storage resources across edge devices and cloud servers. Despite its need for increased storage capacity, the high cost of edge computing, and the inherent limitations of wireless networks connecting edge devices, the CbDQN strategy addresses these challenges. By considering constraints such as limited bandwidth and potential latency issues, it ensures efficient data transfer without compromising performance. The method focuses on mitigating inefficient resource usage, particularly crucial in cloud-based edge computing environments where resource costs are usage-based. To overcome these issues, the CbDQN method efficiently distributes limited resources, optimizing efficiency, minimizing costs, and enhancing overall performance. The approach improves content delivery, reduces latency, and minimizes network congestion. The simulation results substantiate the efficacy of the suggested method in optimizing resource utilization and enhancing system performance, showcasing its potential to address challenges associated with content spreading in IoT edge cloud calculating situations. Our proposed approach evaluated metrics achieves high values of Accuracy is 99.85%, Precision at 99.85%, specificity is 99.82%, sensitivity is 99.82%, F-score is 99.82% and AUC is 99.82%.

Cloud-Edge-End Collaborative Caching Based on Graph Learning for Cyber-Physical Virtual Reality

Virtual reality (VR) is an important landing scenario for 5G and a key enabling technology for the digital economy, and the core challenge it faces is how to ensure real-time interaction and content distribution efficiency. Existing solutions rely on adaptive delivery, overall content edge caching, or asynchronous rendering in the cloud, with drawbacks such as high caching and computing costs and difficulty in ensuring real time. This article is oriented to content generation and synchronization of virtual reality, and aims at realizing intelligent transmission of VR content with high efficiency and low delay guarantee, and carries out research on cloud-edge-end collaborative mechanisms. First, for AI-aided VR content generation, a cloud-edge-end collaborative service architecture is constructed to support independent encoding and distribution of background and interactive content generation. Second, to address the issue that the MEC node with limited cache space cannot meet the dynamical demands of VR user, a cloud-edge collaborative caching strategy based on graph neural networks is proposed to achieve optimal caching and updating of background content, in which the background content is first cached MEC node according to the content request with node sharing scheme, then the content is updated according to a minimal cost update algorithm based on the graph neural networks (GNN)-aided request prediction. Finally, the proposed algorithm is simulated and tested, the simulation results show that the proposed caching and update algorithm achieve better quality of experiment (QoE) and higher cache hit ratio compared with comparison algorithms.

NDN-Based Coded Caching Strategy for Satellite Networks

To solve the transmission correlation issue arising from traditional named data networking (NDN) caching during segmenting contents, where users must obtain data blocks related to the requested file blocks to recover the source data, in this paper, we propose an NDN-based coded caching strategy for low Earth orbit (LEO) satellite networks, using coding operations to remove the transmission correlation of data. To achieve efficient content distribution, the satellite node’s coded package is strategically placed with the objectives of minimizing the backhaul link load and content acquisition latency. The optimization problem is solved by using a multi-colony ant colony algorithm, enabling fast content retrieval. We designed a cluster cooperative distribution mechanism to simplify the satellite network’s management and reduce the load of satellite links for content distribution when covered by multiple satellite nodes. Finally, we compare the multi-objective coded caching (CCMO) introduced in this paper with the most popular (MP) strategy and the random-based coded caching (CCR) strategy; the average cache hit ratio, backhaul link load, and content acquisition delay are compared and analyzed. The results show that CCMO performs better.

Joint optimization of optical path provisioning and VNF placement in vCDN

We consider a joint optimization problem of optical network resources and virtual network function (VNF) placement for efficient content distribution. Current content distribution networks (CDNs) are tightly coupled with network function virtualization (NFV) technologies. A virtual CDN (vCDN) has been intensively investigated to efficiently cope with unpredictable traffic demand. In vCDN, CDN functions are virtually provided as VNF, and we can provide sufficient flexibility regarding the usage of compute resources under traffic demand changes. For the cost-effective CDN services, we must reduce the redundant usage of network resources while improving the efficiency of compute resources. However, a conventional optimal VNF placement technique in vCDN was focused on the efficiency of compute resources, and this can lead to an increase in network cost. To address this issue, we formulate the optimization problem as mixed integer linear programming and then propose a heuristic algorithm called a light-weight VNF placement algorithm in vCDN (LP-vCDN) for reducing network cost while effectively utilizing compute resources. We conducted intensive numerical experiments and demonstrated that LP-vCDN always found solutions of sufficient quality with practical computational overhead.

QoE-Aware Efficient Content Distribution Scheme For Satellite-Terrestrial Networks

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

Satellite multi-beam multicast support for an efficient community-based CDN

The design and implementation of an efficient end-to-end IP-based infrastructure for the delivery of multimedia content is of paramount importance in current public networks, dominated by the constant growth of video streaming traffic. Content Delivery Networks (CDNs) represent the main technological solution to manage the huge volumes of traffic involved, by guaranteeing high quality levels to applications and low impact to the core networks, thanks to the efficient distribution of content among edge caches that are located as close as possible to end-users. Nonetheless, the hierarchical data distribution associated with CDNs can be in principle subject to inefficiencies, as well as performance limitations in case of congested segments along the end-to-end delivery path. In this context, we propose the exploitation of satellite multicast capabilities offered by modern high throughput satellite (HTS) platforms, in a virtualized-compatible model, to compensate for flaws of terrestrial networks. The role of satellite communication is to offer multicast support (as a complement to landline connectivity) with wide geographical service areas based on the available satellite beams, enabling a popularity-based content distribution support. In addition, multi-beam satellite technology allows for a more fine-grained approach to popularity evaluation based on user location. The proposed service and the related network configuration are described in the paper, in relation with current and future SatCom platforms. We then present the results of the proposed CDN caching algorithms in a simulated environment, showing promising results associated with a preliminary performance evaluation.

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

Copa-ICN: Improving Copa as a Congestion Control Algorithm in Information-Centric Networking

To fundamentally improve the efficiency of content distribution in the network, information-centric networking (ICN) has received extensive attention. However, the existence of a large number of IP facilities in the current network makes the smooth evolution of the network architecture a realistic requirement. The ICN architecture that separates the process of name resolution and message routing is widely accepted for its better compatibility with IP networks. In this architecture, the user first obtains the locator of the content replica node from the name resolution system (NRS) and then completes the data transmission through the locator. In data transmission, receiver-driven congestion control algorithms need to be studied. Therefore, we introduce the Copa algorithm into ICN and propose an improved Copa-ICN algorithm. Experiments show that the Copa-ICN algorithm has a high convergence speed and fairness, and when there is a transmission process in the opposite direction, it can still have a high throughput different from the original Copa algorithm.

A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking

As a new network architecture, information-centric networking (ICN) decouples the identifiers and locators of network entities and makes full use of in-network cache technology to improve the content distribution efficiency. For reliable multicast, ICN in-network cache can help reduce the loss recovery delay. However, with the development of applications and services, a multicast tree node often serves multiple reliable multicast groups. How to reasonably allocate cache resources for each multicast group will greatly affect the performance of reliable multicast. In order to improve the overall loss recovery performance of reliable multicast, this paper designs a dynamic cache allocation mechanism (DCAM). DCAM considers the packet loss probability, the node depth of the multicast tree, and the multicast transmission rate of multicast group, and then allocates cache space for multicast group based on the normalized cache quota weight. We also explore the performance of three cache allocation mechanisms (DCAM, AARM, and Equal) combined with four cache strategies (LCE, CAPC, Prob, and ProbCache), respectively. Experimental results show that DCAM can adjust cache allocation results in time according to network changes, and its combinations with various cache strategies outperform other combinations. Moreover, the combination of DCAM and CAPC can achieve optimal performance in loss recovery delay, cache hit ratio, transmission completion time, and overhead.

PSO‐based traffic scheduling mechanism in information‐centric networking

In large‐scale network mode, congestion may occur in any link at any time, so traffic scheduling becomes a research hotspot. This paper pays attention to the traffic scheduling method in information‐centric networking (ICN). ICN transforms the host‐oriented end‐to‐end communication mode in the traditional network architecture into an information‐centric network architecture, which uses the network in‐network caching to significantly improve the content distribution efficiency. This is applied to traffic scheduling through the introduction of artificial intelligence (AI). Therefore, this paper proposes a traffic scheduling strategy based on particle swarm optimization (PSO) algorithm in ICN. The experimental results demonstrate that the proposed traffic scheduling algorithm in this paper outperforms three benchmarks in terms of average latency, packet loss rate and average throughput.

Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

PB-NCC: A Popularity-Based Caching Strategy with Number-of-Copies Control in Information-Centric Networks

The Information-Centric Network (ICN), designed for efficient content acquisition and distribution, is a promising candidate architecture for the future Internet. In-network caching in ICN makes it possible to reuse contents and the Name Resolution System (NRS) makes cached contents better serve users. In this paper, we focused on the ICN caching scenario equipped with an NRS, which records the positions of contents cached in ICN. We propose a Popularity-based caching strategy with Number-of-Copies Control (PB-NCC) in this paper. PB-NCC is proposed to solve the problems of unreasonable content distribution and frequent cache replacement in traditional caching strategies in ICN. We examine PB-NCC with a large number of experiments in different topologies and workloads. The simulation results reveal that PB-NCC can improve the cache hit ratio by at least 8.85% and reduce the server load by at least 11.34% compared with other on-path caching strategies, meanwhile maintaining a low network latency.

Flow control oriented forwarding and caching in cache-enabled networks

The cache-enabled network architecture is very promising to improve the efficiency of content distribution and reduce the network congestion. In this paper, we propose a maximizing weighted throughput (MWT) algorithm for joint request forwarding, cache placement and flow control to dynamically optimize network performance. Specifically, a dual queue system that includes requests and data is established to retrieve the global content demands and traffic congestion information. In order to improve throughput performance as well as stabilize the queue, we formulate the flow-level throughput and design the request-level control policy by optimizing the throughput function and Lyapunov drift. The request forwarding policy adaptively allocates request forwarding rates for every link according to the differences among adjacent request queue backlogs. A novel cache priority function and a threshold-based request-dropping policy are used in the caching policy and flow control respectively to alleviate network overload. In addition, we prove the MWT algorithm achieves throughput near-optimal performance, and testify the dual queue system is stable by deducing the upper bound of the all queue backlogs. The experimental results verify the MWT algorithm stability and demonstrate the superiority of the MWT algorithm, compared to the state-of-the-art caching algorithms which are combined with back-pressure algorithm.

NB-Cache: Non-Blocking In-Network Caching for High-Performance Content Routers

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to in-network caching and native multicast. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). In this work, we build an analytical model of the router and identify that CS is the actual bottleneck. Then, we propose a novel mechanism called “NB-Cache” to address CS’s performance issue from a network-wide point of view. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. NB-Cache follows a design pattern of on-demand load balancing and can be formulated into a non-trivial N-queue bypass model. We use the Markov chain to establish its theoretical base and find an algorithm for automated transition rate matrix generation. Experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput. NB-Cache decouples the fast packet forwarding from the slower content retrieval thus substantially reducing CS’s heavy dependency on fast but expensive memory.

A New Efficient Cache Replacement Strategy for Named Data Networking

The Information-Centric Network (ICN) is a future internet architecture with efficient content retrieval and distribution. Named Data Networking (NDN) is one of the proposed architectures for ICN. NDN’s innetwork caching improves data availability, reduce retrieval delays, network load, alleviate producer load, and limit data traffic. Despite the existence of several caching decision algorithms, the fetching and distribution of contents with minimum resource utilization remains a great challenge. In this paper, we introduce a new cache replacement strategy called Enhanced Time and Frequency Cache Replacement strategy (ETFCR) where both cache hit frequency and cache retrieval time are used to select evicted data chunks. ETFCR adds time cycles between the last two requests to adjust data chunk’s popularity and cache hits. We conducted extensive simulations using the ccnSim simulator to evaluate the performance of ETFCR and compare it to that of some well-known cache replacement strategies. Simulations results show that ETFCR outperforms the other cache replacement strategies in terms of cache hit ratio, and lower content retrieval delay.

Secure Content Distribution with Access Control Enforcement in Named Data Networking.

NDN is one of the new emerging future internet architectures which brings up new solutions over today’s internet architecture, facilitating content distribution, in-network caching, mobility support, and multicast forwarding. NDNs ubiquitous in-network caching allows consumers to access data directly from the intermediate router’s cache. However, it opens content privacy problems since data packets replicated in the router are always accessible by every consumer. Sensitive contents in the routers should be protected and accessed only by authorized consumers. Although the content protection problem can be solved by applying an encryption-based access control policy, it still needs an efficient content distribution scheme with lower computational overhead and content retrieval time. We propose an efficient and secure content distribution (ES_CD), by combining symmetric encryption and identity-based proxy re-encryption. The analysis shows that our proposed scheme achieves content retrieval time reduction up to 20% for the cached contents in our network simulation environment and a slight computational overhead of less than 19 ms at the content producer and 9 ms at the consumer for 2 KB content. ES_CD provides content confidentiality and ensures only legitimate consumers can access the contents during a predefined time without requiring a trusted third party and keeping the content producer always online.

Handling Tradeoff Between Visual Perception and Compression in Video Codec Using Optimal Intra-Frame Prediction

Video coding and compression play a key role in efficient transmission and distribution of digital video content with comparatively lower storage and bandwidth requirements. However, the current research effort and evolutionary aspects still expect to develop a better solution that can jointly address the time complexity, quality measures, and data transfer rate problem in digital video transmission and distribution. Considering this, the study introduces an optimal video codec model to handle the tradeoff between visual perception and compression issues in video coding by introducing a novel optimized modeling of the intra-frame prediction method. The method's design and development are extensively carried out from the analytical viewpoint, where it also emphasizes reducing the encoding time to a significant extent. The experimental, numerical analysis shows that the proposed modeling of the optimized estimation of intra-frame prediction outperforms the conventional approach in terms of visual perception evaluated concerning Peak Signal-To-Noise Ratio (PSNR), bit rate and encoding time.