Content Caching Scheme Research Articles

Cooperative Edge Caching Based on Temporal Convolutional Networks

With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching schemes do not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve this problem, we design a new learning-based cooperative caching strategy (LECS) that encompasses three key components. Firstly, a temporal convolutional network driven content popularity prediction model is developed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content cached on a given edge server. Thirdly, an novel dynamic programming algorithm is developed to maximize the overall CCV. Extensive simulation results have demonstrated the superiority of our approach. Compared with the state-of-the-art caching schemes, LECS can improve the cache hit rate by 8.3%-10.1%, and reduce the average content delivery delay by 9.1%-15.1%.

Intelligent Content Caching Strategy in Autonomous Driving Toward 6G

The rapid development of 6G can help to bring autonomous driving closed to the reality. Drivers and passengers will have more time for work and leisure spending in the vehicles, further generating a lot of data requirements. However, edge resources from small base stations are insufficient to match the wide variety of services of the future vehicular networks. Besides, due to the high-speed nature of the vehicles, users have to switch the connections among different base stations, whereas such way will cause external latency during the data request. Therefore, it is vital to enable the local cache of vehicle users to realize the reliable autonomous driving. In this paper, we consider caching the contents in the local cache, small base station, and edge server. In practice, the request preference of some single users may be different from a whole region. To maximize the efficiency of content cache, we design a strategy that uses reinforcement learning algorithm to optimize cache schemes on different devices. The experimental results demonstrate that our strategy can enhance the cache hit ratio by 10%-20% compared with the well-known counterparts.

Multi-UAV Content Caching Strategy and Cooperative, Complementary Content Transmission Based on Coalition Formation Game.

The transmission of a large amount of video and picture content brings more challenges to wireless communication networks. Unmanned aerial vehicle (UAV)-aided small cells with active content caching deployed on cellular networks are recognized as a promising way to alleviate wireless backhaul and support flexible coverage. However, a UAV cannot operate for a long time due to limited battery life, and its caching capacity is also limited. For this, a multi-UAV content-caching strategy and cooperative, complementary content transmission among UAVs are jointly studied in this paper. Firstly, a user-clustering-based caching strategy is designed, where user clustering is based on user similarity, concurrently taking into consideration similarities in content preference and location. Then, cooperative, complementary content transmission between multiple UAVs is modeled as a coalition formation game (CFG) to maximize the utility of the whole network. Finally, the effectiveness of the proposed algorithms is demonstrated through numerical simulations.

User-Aware and Flexible Proactive Caching Using LSTM and Ensemble Learning in IoT-MEC Networks

To meet the stringent demands of emerging Internet-of-Things (IoT) applications, such as smart home, smart city, and virtual reality in 5G/6G IoT networks, edge content caching for mobile/multiaccess edge computing (MEC) has been identified as a promising approach to improve the quality of services in terms of latency and energy consumption. However, the limitations of cache capacity make it difficult to develop an effective common caching framework that satisfies diverse user preferences. In this article, we propose a new content caching strategy that maximizes the cache hit ratio through flexible prediction in dynamically changing network and user environments. It is based on a hierarchical deep learning architecture: long short-term memory (LSTM)-based local learning and ensemble-based meta-learning. First, as a local learning model, we employ an LSTM method with seasonal-trend decomposition using loess (STL)-based preprocessing. It identifies the attributes for demand prediction on the contents in various demographic user groups. Second, as a metalearning model, we employ a regression-based ensemble learning method, which uses an online convex optimization framework and exhibits sublinear “regret” performance. It orchestrates the obtained multiple demographic user preferences into a unified caching strategy in real time. Extensive experiments were conducted on the popular MovieLens data sets. It was shown that the proposed control provides up to a 30% higher cache hit ratio than conventional representative algorithms and a near-optimal cache hit ratio within approximately 9% of the optimal caching scheme with perfect prior knowledge of content popularity. The proposed learning and caching control can be implemented as a core function of the 5G/6G standard’s network data analytic function (NWDAF) module.

Two-Layer Distributed Content Caching for Infotainment Applications in VANETs

For vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (VANETs), edge caching has attracted considerable research attention to maximize the efficiency and reliability of infotainment applications. In this article, we propose a two-layer distributed content caching scheme for VANETs by jointly exploiting the cache at both vehicles and roadside units (RSUs). Specifically, we formulate the content caching problem to minimize the overall transmission delay and cost as a nonlinear integer programming (NLIP) problem and propose an alternate dynamic programming search (ADPS)-based algorithm to solve it. In ADPS, we divide the original problem into three subproblems and then we use the dynamic programming (DP) method to solve each subproblem separately. To reduce the complexity, we further propose a cooperation-based greedy (CBG) algorithm to solve the large-scale original problem. Both numerical simulation results and experiments in the testbed show that the proposed caching scheme outperforms existed caching schemes, and the transmission delay and cost can be reduced by 10% and 24%, respectively, while the hit ratio can be increased by 30% in a practical environment, as compared to the popularity-based caching scheme.

An energy efficient and scalable endpoint linked green content caching for Named Data Network based Internet of Things

Technology and applications of Internet of Things (IOT) is expanding. Large number of smart devices with networking capabilities, often called endpoints are part of IOT networks. These endpoints are typically resource constrained in terms of battery power and this impacts the endurance of IOT applications in operational field. Therefore, energy effciency of IOT networks is important. This paper employs content caching mechanism of Named Data Network (NDN) as leverage to propose a novel Endpoint Linked Green Content Caching scheme to reduce energy consumption in NDN based IOT network architectures. We implement a novel threshold based content offloading mechanism where endpoints can adapt to increase in content request rate in real time, conserve energy and collect metadata required to maintain the freshness of transient data. The proposed scheme achieved reduction in network energy consumption in the range of 30%–57% and reduction in response time for content request in the range of 35%–75% when compared with different existing schemes. The strategy performed consistently when the number of nodes in the network was scaled.

Research on Route Pre-caching Based on Content Type Awareness

In order to improve cache hit efficiency and reduce network response delay in Vehicular Named Data Networking (VNDN) environment, a routing strategy based on content type awareness and a collaborative content pre-caching strategy based on content popularity prediction for probabilistic caching were proposed. Firstly, an appropriate route-forwarding strategy is selected according to the message characteristics and whether the destination node is known. Then, unsupervised learning topic model Latent Dirichlet Allocation (LDA) is used to dynamically predict the request preference of vehicle users. Secondly, the topological relationship between different devices in the Internet of vehicles and the predicted vehicle user preferences are used to accurately and effectively predict the popularity of content, so as to reduce the redundancy of content files cached by network devices and maximize the cache hit ratio. Simulation results show that compared with other content caching strategies, this content pre-caching strategy can effectively improve cache performance.

Route-Based Proactive Content Caching Using Self-Attention in Hierarchical Federated Learning

The sheer unpredictability of content popularity, diversified user preferences and demands, and privacy concerns for data sharing all create hurdles to develop proactive content caching strategies in self-driving cars. Therefore, to address these concerns, we investigate in detail the role of proactive content caching methods in self-driving cars for improving quality-of-experience (QoE) and content retrieval cost in this work. We develop a low-complexity content popularity prediction mechanism in a hierarchical federated setting. In particular, we use a self-attention technique with an LSTM-based prediction mechanism to extract local content popularity patterns in self-driving cars. However, the local contents will not be sufficient to satisfy the passenger’s requirements. Hence, using the popular contents of other self-driving cars will solve the requirement constraint but poses some privacy issues. We use the privacy-preserving decentralized model training framework of Federated Learning (FL) to tackle this issue. Specifically, we deploy the hierarchical Federated Averaging (FedAvg) algorithm on local models obtained from self-driving cars to develop a regional and global content popularity prediction model at RSU and MBS, respectively. Extensive simulations on real-world datasets show the proposed approach improves cache space utilization by maximizing the local cache hit ratio, and further, minimizes the content retrieval cost for self-driving cars as compared with alternative methods.

Socially Driven Joint Optimization of Communication, Caching, and Computing Resources in Vehicular Networks

To support multifarious vehicular applications, content sharing among vehicles or between vehicles and infrastructures can enable efficient service provisioning. In this work, we investigate joint communication, caching, and computing (3C) resource allocation to support efficient content sharing between content providers (CPs) and content requesters (CRs) in vehicular networks. To tackle the high complexity of joint 3C resource allocation, we decouple the problem into a long-term content caching strategy to allocate caching resources plus a method for short-term CP-CR pairing and corresponding communication-computing resource allocation. Specifically, a popularity and social similarity (P-SS) based caching strategy is proposed by incorporating both physical and social information. As selfish CRs may refuse to reveal their quality of service (QoS) requirements to the CPs and lead to the information asymmetry, we adopt contract theory to allocate communication and computing resources for each potential CR-CP pair. We then propose a stable-matching based algorithm to match CPs and CRs for efficient content sharing. Simulation results verify that the proposed scheme can effectively solve the problem with low complexity.

New Collaborative Caching Scheme for D2D Content Sharing in 5G

Device-to-Device (D2D) content sharing is an excellent solution to offload the traffic from the core 5G networks and backhaul links. Caching the most popular files at the network edge and in users’ devices to support user proximity services in 5G helps to offload traffic in the core network and increases the cache hit probability. D2D communication in 5G can be utilized to share the cached files between a pair of devices with a minimal involvement from the base station. This paper presents a collaborative content caching scheme with the primary objective to maximize the overall system offloading gain and the cache hit probability in downloading the popular file contents. The proposed system model exploits the social-networking concept within the WiFi Direct radio range, assuming the cell structure present in a densely populated area such as a university campus or an auditorium. The performance of the proposed cache scheme is evaluated through extensive simulations taking into consideration the device mobility and disconnection, and the results are presented.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN.

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications.

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

Outage Performance of 3D Mobile UAV Caching for Hybrid Satellite-Terrestrial Networks

In this paper, we consider a hybrid satellite-terrestrial network (HSTN) where a multiantenna satellite communicates with a ground user equipment (UE) with the help of multiple cache-enabled amplify-and-forward (AF) three-dimensional ($3$D) mobile unmanned aerial vehicle (UAV) relays. Herein, we employ the two fundamental most popular content (MPC) and uniform content (UC) caching schemes for two types of mobile UAV relays, namely fully $3$D and fixed height. Taking into account the multiantenna satellite links and the random $3$D distances between UAV relays and UE, we analyze the outage probability (OP) of considered system with MPC and UC caching schemes. We further carry out the corresponding asymptotic OP analysis to present the insights on achievable performance gains of two schemes for both types of $3$D mobile UAV relaying. Specifically, we show the following: (a) MPC caching dominates the UC and no caching schemes; (b) fully $3$D mobile UAV relaying outperforms its fixed height counterpart. We finally corroborate the theoretic analysis by simulations.

Incentive-Driven Deep Reinforcement Learning for Content Caching and D2D Offloading

Offloading cellular traffic via Device-to-Device communication (or D2D offloading) has been proved to be an effective way to ease the traffic burden of cellular networks. However, mobile nodes may not be willing to take part in D2D offloading without proper financial incentives since the data offloading process will incur a lot of resource consumption. Therefore, it is imminent to exploit effective incentive mechanisms to motivate nodes to participate in D2D offloading. Furthermore, the design of the content caching strategy is also crucial to the performance of D2D offloading. In this paper, considering these issues, a novel Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM is proposed, in which the reverse auction is employed as the incentive mechanism. Then, the incentive-driven D2D offloading and content caching process is modeled as Integer Non-Linear Programming (INLP), aiming to maximize the saving cost of the Content Service Provider (CSP). To solve the optimization problem, the content caching method based on a Deep Reinforcement Learning (DRL) algorithm, named DQN is proposed to get the approximate optimal solution, and a standard Vickrey-Clarke-Groves (VCG)-based payment rule is proposed to compensate for mobile nodes' cost. Extensive real trace-driven simulation results demonstrate that the proposed IDQNM greatly outperforms other baseline methods in terms of the CSP's saving cost and the offloading rate in different scenarios.

An optimized content caching strategy for video stream in edge-cloud environment

With the mobile data traffic grows rapidly and the video data has a high proportion, the backhaul link faces great pressure. The conventional centralized architecture has been far from enough to satisfy the user demands. For reducing user response latency and easing backhaul stress, the streaming media contents should be proactively stored in the edge of network and new contents caching model needs to be presented. In this work, a video content collaborative caching strategy in the cloud-edge cooperative environment is proposed. In this strategy, first, the k-means algorithm is used to cluster the edge servers. Then the latency and caching cost gain bring by caching the content on the edge servers in the cluster and the collaborative cache domain are analyzed to establish the content caching problem. Further, the marginal gain is calculated by analyzing the latency and caching cost gain. Finally, in order to solve the content caching problem, this paper proposed a marginal gain based content caching algorithm. Experimental results prove the effectiveness of the proposed algorithm.

Content Management Based on Content Popularity Ranking in Information-Centric Networks

Users can access the Internet anywhere they go at any time due to the advancement of communications and networking technologies. The number of users and connected devices are rapidly increasing, and various forms of content are becoming increasingly available on the Internet. Consequently, several research ideas have emerged regarding the storage policy for the enormous amount of content, and procedures to remove existing content due to the lack of storage space have also been discussed. Many of the proposals related to content caching offer to identify the popularity of certain content and hold the popular content in a repository as long as possible. Although the host-based Internet has been serving its users for a long time, managing network resources efficiently during high traffic load is problematic for the host-based Internet because locating the host with their IP address is one of the primary mechanisms behind this architecture. A more strategical networking paradigm to resolve this issue is Content-Centric Networking (CCN), a branch of the networking paradigm Information-Centric Networking (ICN) that is focused on the name of the content, and therefore can deliver the requested content efficiently, securely, and faster. However, this paradigm has relatively simple content caching and content removal mechanisms, as it caches all the relevant content at all the nodes and removes the content based on the access time only when there is a lack of space. In this paper, we propose content popularity ranking (CPR) mechanism, content caching scheme, and content removal scheme. The proposed schemes are compared to existing caching schemes such as Leave Copy Everywhere (LCE) and Leave Copy Down (LCD) in terms of the Average Hop Count, content removal schemes such as Least Recently Used (LRU) and Least Frequently Used (LFU) in terms of the Cache Hit Ratio, and finally, the CCN paradigm incorporating the LCE and the LRU schemes and the host-based Internet architecture in terms of Content Delivery Time. Graphical presentations of performance results utilizing the proposed schemes show that the proposed CPR-based schemes for content caching and content removal provide better performance than the host-based Internet and the original CCN utilizing LCE and LRU schemes.

Edge Content Caching with Deep Spatiotemporal Residual Network for IoV in Smart City

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the same contents required by different distributed vehicular users, processing the copies of the same contents repeatedly in an edge server leads to a waste of resources (e.g., storage, computation, and bandwidth) in ESs. Therefore, it is a challenge to provide high-quality services while guaranteeing the resource efficiency with edge content caching. To address the challenge, an edge content caching method for smart cities with service requirement prediction, named E-Cache, is proposed. First, the future service requirements from the vehicles are predicted based on the deep spatiotemporal residual network (ST-ResNet). Then, preliminary content caching schemes are elaborated based on the predicted service requirements, which are further adjusted by a many-objective optimization aiming at minimizing the execution time and the energy consumption of the vehicular services. Eventually, experimental evaluations prove the efficiency and effectiveness of E-Cache with spatiotemporal traffic trajectory big data.

Security-Enhanced Content Caching for the 5G-Based Cognitive Internet of Vehicles

With the explosive growth of multimedia contents in vehicular networks and the development of 5G technology come great challenges to the Internet of Vehicles system. By utilizing the storage capability of roadside units, popular contents can be cached in advance during nonpeak periods, which can bring better quality of experience to users. However, it is difficult for the existing content caching schemes in traditional vehicular networks to achieve global control when designing content caching mechanisms. Moreover, some content caching schemes also bring security concerns. Thus, in this article, we first discuss the content caching problem in the 5G-based cognitive Internet of Vehicles. Then we design security enhancement methods with the cognitive engine. Finally, we use a special case to study how to design a secure and delay-sensitive content caching scheme in cognitive vehicular networks. Extensive experiments show that the proposed algorithm outperforms traditional content caching methods.

Dynamic popularity window and distance-based efficient caching for fast content delivery applications in CCN

Content-Centric Networking (CCN) is being established as an emerging Internet architecture that brings efficient data retrieval technology to access the contents with their names. In CCN, the in-network caching capability plays a critical role in the improvement of network performance by reducing the load of content server and increasing the content availability. Thus, the efficiency of the content caching strategy crucially affects the performance of the entire network. Towards this, a novel content caching scheme has been proposed in this article that efficiently utilizes the available caching capacity and improves Quality-of-Service (QoS) during content delivery. To perform optimized caching operations, the proposed scheme considers a dynamic threshold value and takes caching decisions based on two parameters, first is a novel dynamic size popularity window to determine content popularity, and secondly, the distance navigated by the Content message from the preceding on-path router that cached the content copy. For content caching decisions, the dynamic threshold value decreases with an increase in Content message distance from the preceding on-path router (that cached the content) and the content popularity. Using these heuristics, the scheme places the popular contents near the edges of the network. Through extensive simulations, the QoS delivered by the proposed scheme has been examined for different cache sizes on standard Abilene network topology. The results show that the proposed scheme outperforms the existing caching strategies for various performance parameters such as hit-ratio, average network delay, hop count, and average traffic generated in the network. This makes the proposed solution suitable for futuristic Internet architectures with 5G/6G and Industry 4.0 revolutions and their applications where faster content deliveries are required.

A Privacy-Preserving Caching Scheme for Device-to-Device Communications

With device-to-device (D2D) communication, user equipment can share data with each other without the involvement of network infrastructures. In order to maintain the Quality of Service (QoS) and Quality of Experience (QoE) for user applications in D2D communications, most existing schemes use proactive content caching that needs to predict content popularity before making caching decisions which may result in privacy leakage, since the information of users is collected to train a deep learning-based model to predict content popularity. Therefore, it is crucial to guarantee secure data collection in machine learning-based framework. In this paper, we propose a privacy-preserving D2D caching scheme with a passive content caching strategy based on node importance, which can deliver more efficient caching and prevent the potential leakage of user privacy. The scheme is based on softwaredefined networking (SDN), in which the controller is responsible for calculating node importance of devices according to the information of requests and encounters collected by SDN switches. Base station will decide which device can establish reliable and secure communication with content requester based on historical information. The simulation results show that the proposed strategy can outperform other D2D caching strategies in terms of cache hit rate and data rate.