Proactive Content Caching Research Articles

Fine-Grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks

The 5 G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5 G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76 m, with up to 80% of the unknown areas.

User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets

User Preferences-Based Proactive Content Caching with Characteristics Differentiation in HetNets

DeepFESL: Deep Federated Echo State Learning-Based Proactive Content Caching in UAV-Assisted Networks

DeepFESL: Deep Federated Echo State Learning-Based Proactive Content Caching in UAV-Assisted Networks

Proactive Content Caching Scheme in Urban Vehicular Networks

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

Mobility-Aware Proactive Edge Caching for Large Files in the Internet of Vehicles

By shifting the requested content to the edge in the Internet of Vehicles (IoV), edge caching is expected to be an effective solution to satisfy the low latency and high reliability requirements of IoV users for multimedia services. However, the edge node’s coverage area and storage space are limited. Moreover, since vehicles have high mobility and in-vehicle multimedia applications require sequential delivery for contents, we need to address two main issues: 1) How to optimize the proactive content caching decision (i.e., the placement of cached content chunks) among edge nodes (ENs) to provide better Quality of Services (QoS) for IoV users. 2) How to ensure that vehicles can download the required contents sequentially to improve Quality of Experience (QoE). In this paper, we propose a mobility-aware proactive edge caching scheme (MSTPS), where the spatial and temporal prediction of vehicles are taken into account for content deployment and scheduling. Specifically, we optimize the caching decision based on predicting the vehicle’s driving trajectory and travel preference. The scheme learns the vehicle’s travel preferences to cope with mobility uncertainty by combining users with similar travel patterns. Meanwhile, the proposed scheme can support the sequential downloading of content chunks. Furthermore, in order to deal with the dynamic characteristics and unpredictable challenges of the IoV, we design a system recovery strategy, which can avoid the degradation of the proposed scheme due to the failure of prediction. Finally, by using real mobility datasets and scenarios, we explore the impact of the number of ENs deployed in advance for each vehicle’s request when the cache needs to be updated on system performance. In addition, we evaluate the effectiveness of the proposed scheme. Our proposed scheme can achieve the best cache hit ratio and decrease caching costs compared to the existing mobility-aware caching schemes.

An autonomous proactive content caching method in edge computing environment: a learning-based approach

An autonomous proactive content caching method in edge computing environment: a learning-based approach

Mobility-Based Proactive Cache and Transmission Strategy in Millimeter Wave Heterogeneous Networks

Millimeter wave (mmWave) frequencies with rich bandwidth can greatly alleviate the surging traffic demand of wireless communication from mobile video streaming. However, the harsh propagation environment at mmWave bands and user mobility make it hard to provide a reliable service. Multibeam transmissions can be taken as an essential approach for maintaining a robust mmWave link. Meanwhile, the introduction of coded caching can further reduce the end-to-end latency and backhaul traffic. Considering the interaction between caching and transmission in mobility scenario, we develop a joint design for mmWave heterogeneous networks (HetNets), by combining multibeam transmission and maximum distance separable (MDS)-coded caching. First, a joint optimization problem of cache placement and beam pair selection is formulated, in order to maximize the backhaul traffic offload in mmWave HetNets. To solve the mixed-timescale mixed-integer nonlinear programming (MINLP) problem, we then decompose the joint problem into the long-term mobility-based proactive content caching (CC) and short-term content delivery (CD) subproblems. Subsequently, two many-to-many matching-based algorithms are proposed for tackling the CD subproblem and the convex technique is applied to deal with the CC subproblem. Simulation results demonstrate the efficacy of the proposed algorithms over benchmark schemes.

Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing

With the aggravation of data explosion and backhaul loads on 5 G edge network, it is difficult for traditional centralized cloud to meet the low latency requirements for content access. The federated learning ( <u>F</u> L)-based <u>p</u> roactive content <u>c</u> aching (FPC) can alleviate the matter by placing content in local cache to achieve fast and repetitive data access while protecting the users’ privacy. However, due to the non-independent and identically distributed (Non-IID) data across the clients and limited edge resources, it is unrealistic for FL to aggregate all participated devices in parallel for model update and adopt the fixed iteration frequency in local training process. To address this issue, we propose a distributed resources-efficient FPC policy to improve the content caching efficiency and reduce the resources consumption. Through theoretical analysis, we first formulate the FPC problem into a stacked autoencoders (SAE) model loss minimization problem while satisfying resources constraint. We then propose an adaptive FPC (AFPC) algorithm combined deep reinforcement learning (DRL) consisting of two mechanisms of client selection and local iterations number decision. Next, we show that when training data are Non-IID, aggregating the model parameters of all participated devices may be not an optimal strategy to improve the FL-based content caching efficiency, and it is more meaningful to adopt adaptive local iteration frequency when resources are limited. Finally, experimental results in three real datasets demonstrate that AFPC can effectively improve cache efficiency up to 38.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 6.84 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , and save resources up to 47.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 35.6 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , respectively, compared with traditional multi-armed bandit (MAB)-based and FL-based algorithms.

Uncoded Caching and Cross-Level Coded Delivery for Non-Uniform File Popularity

Proactive content caching at user devices and coded delivery is studied for a non-uniform file popularity distribution. A novel centralized uncoded caching and coded delivery scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-level coded delivery (CLCD)</i> , is proposed, which can be applied to large file libraries under non-uniform demands. In the CLCD scheme, the same sub-packetization is used for all the files in the library in order to prevent additional zero-padding in the delivery phase, and unlike the existing schemes in the literature, users requesting files from different popularity groups can still be served by the same multicast message in order to reduce the delivery rate. Simulation results indicate more than 10% reduction in the average delivery rate for typical Zipf distribution parameter values.

Energy-Efficient Proactive Content Caching For On-Demand Video Streaming Under Uncertainty

Proactive content caching using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge service providers</i> (ESPs) present in wireless heterogeneous networks is an emerging research topic to deal with stringent client requirements. The limited computing resources of an ESP (e.g., small-cell base stations) can be utilized for transcoding the high resolution contents and thereby supporting the bit rate requirements of the clients. Unlike existing works, this paper deals with the case where the storage and computing capacities of the ESPs are random and time varying (e.g., due to internal workloads). This aspect is important because a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content provider</i> (CP) cannot be aware of the exact resource availability at an ESP when determining proactive content placement. Different from existing works, we also ensure that the bit rate requirements of the clients are maintained throughout their video streaming sessions. Further, uncertainties in the demand (i.e., number of content requests) and link capacities are also considered. Given these unique challenges, we propose a two-stage stochastic problem which jointly determines the content placement decisions and demand allocation decisions for the CP under various uncertainties. The performance of the proposed model is rigorously analyzed in terms of energy consumption and percentage demand allocation, and compared to some benchmark models through extensive simulations.

Proactive Content Caching Based on Actor–Critic Reinforcement Learning for Mobile Edge Networks

Mobile edge caching/computing (MEC) has emerged as a promising approach for addressing the drastic increasing mobile data traffic by bringing high caching and computing capabilities to the edge of networks. Under MEC architecture, content providers (CPs) are allowed to lease some virtual machines (VMs) at MEC servers to proactively cache popular contents for improving users’ quality of experience. The scalable cache resource model rises the challenge for determining the ideal number of leased VMs for CPs to obtain the minimum expected downloading delay of users at the lowest caching cost. To address these challenges, in this paper, we propose an actor-critic (AC) reinforcement learning based proactive caching policy for mobile edge networks without the prior knowledge of users’ content demand. Specifically, we formulate the proactive caching problem under dynamical users’ content demand as a Markov decision process and propose a AC based caching algorithm to minimize the caching cost and the expected downloading delay. Particularly, to reduce the computational complexity, a branching neural network is employed to approximate the policy function in the actor part. Numerical results show that the proposed caching algorithm can significantly reduce the total cost and the average downloading delay when compared with other popular algorithms.

Federated learning empowered mobility-aware proactive content offloading framework for fog radio access networks

Proactive content caching has emerged as a promising solution to cope with exponentially increasing mobile data traffic. The popular user contents can be cached near the network edge for faster retrieval and processing. Current state-of-the-art approaches adopt a centralized model training mechanism that requires high communication and data exchange overheads to predict content popularity. Moreover, these approaches fail to deal with the dynamicity of the environment since they do not take into account the users’ mobility information and are unable to incorporate content offload timings. In this paper, we address these limitations by proposing a novel federated learning-based Mobility and Demand-aware Proactive Content Offloading (MDPCO) framework. MDPCO exploits distributed learning strategies and capitalizes on users’ mobility and demand information for proactive content offloading. Extensive simulations are carried out to validate the efficacy of MDCPO against local and cloud-based models. Our proposed model yields an average performance improvement of 6.7% in comparison to the cloud-based model. Furthermore, with the increase in the number of fog servers, the MDPCO achieves a 9.8% higher data offloading ratio and 1.18% increase in the downlink rates while being more energy-efficient than cloud-based approaches.

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.

Proactive content caching in edge computing environment: A review

AbstractEdge computing environment provides processing capability and computing at the network edge and close to users. Edge equipment includes small data centers that locally perform process and content delivery. Therefore, edge equipment management has received much attention due to the rapid growth of information and resources limitations. The content caching and proactive caching techniques are management methods of edge equipment resources. We recently witnessed the development of proactive caching mechanisms that have a crucial enabler in improving heavy traffic, energy, and bandwidth. Also, it has huge potential to increase the quick response time to users' requests that today, these services are demanded by many users and applications. This article prepares a systematic literature review content caching approach in the edge computing environment. The purpose of this study is to survey the research done on the proactive caching strategies in the edge computing environment to identify subjects that must be emphasized more in current and future research paths. This research has studied 71 articles divided into three classes: model‐based, machine‐learning‐based, and heuristic‐based. Next, we discuss content caching approaches based on critical factors such as performance metrics, case studies, utilized techniques, assessment tools, advantages, and disadvantages. Finally, open issues and challenges are presented, and the survey is concluded.

Multi-Objective Caching Optimization for Wireless Backhauled Fog Radio Access Network

Proactive content caching in a fog radio access network (F-RAN) is an efficient technique used to alleviate delivery delay and traffic congestion. However, the symmetric caching of the content is impractical due to the dissimilarity among the contents popularity. Therefore, in this paper, a multi-objective random caching scheme to balance the successful transmission probability (STP) and delay in wireless backhauled F-RAN is proposed. First, stochastic geometry tools are utilized to derive expressions of the association probability, STP, and average delivery delay. Next, the complexity is reduced by considering the asymptotic STP and delay in the high signal-to-noise ratio (SNR) regime. Then, aiming at maximizing the STP or minimizing the delay, the multi-objective cache placement optimization problem is formulated. A novel projected multi-objective cuckoo search algorithm (PMOCSA) is proposed to obtain the Pareto front of the optimal cache placement. The numerical results show that PMOCSA outperforms the original multi-objective cuckoo search algorithm (MOCSA) in terms of convergence to a feasible Pareto front and its rate. It also shows that the proposed multi-objective caching scheme significantly outperforms the well-known benchmark caching schemes by up to 40% higher STP and 85% lower average delay.

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.

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user’s quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.

Content Caching Oriented Popularity Prediction: A Weighted Clustering Approach

Content popularity prediction plays an important role on proactive content caching. Different to most of the existing works which focus on improving the popularity prediction accuracy, in this article, we consider the content caching oriented popularity prediction through a weighted clustering approach in order to improve the caching performance. We formulate the loss of the cache hit ratio as the system regret to indicate the caching performance, and construct a clustering-based popularity prediction framework for overcoming the user request sparsity with considering the similarity of popularity evolution trends. For depicting the explicit relationship between the caching performance and the popularity prediction accuracy, we derive the popularity prediction error distribution of each content, and design the caching threshold. By extracting the insights in the relationship between the popularity prediction accuracy and the user clustering strategy, we develop a weighted clustering-based popularity prediction algorithm, which takes the caching regret probability of files as the weights. Based on two real-world datasets, the simulation results demonstrate that the proposed popularity prediction scheme achieves better caching performance than the state-of-the-art schemes.

Social-Aware Proactive Content Caching and Sharing in Multi-Access Edge Networks

The ever-increasing data explosion, particular mobile video traffic, increases the backhaul load and makes it difficult for the centralized cloud to meet the requirements of various services. Accordingly, proactive caching at mobile devices gains more attentions, which is envisioned as a promising technology to relieve the backhaul traffic and cater to diverse quality of service in multi-access edge networks. In this article, we study the problem of proactive caching and content sharing for mobile devices in multi-access edge networks. Since users’ mobility behaviors and social impacts on content preference distort the performance of content requests, a long-term strategy of proactive caching is proposed to minimize the sum of communication costs to get the requested contents. By taking the mutual interaction of devices into account, we design a local cooperative utility function that is used to derive a distributed proactive content caching algorithm using game theory. Theoretical analysis is provided to confirm the stability and optimality of the proposed algorithm. Furthermore, simulation results show that the proposed algorithm converges efficiently and, most importantly, it outperforms compared to no local cooperation caching scheme, non-social aware caching scheme and randomly caching scheme for various kinds of user behaviors and device caching capacity.

QoE Analysis in Cache-Enabled Multi-UAV Networks

This paper presents a quality of experience (QoE)-guided content delivery framework in cache-enabled multi-unmanned aerial vehicle (UAV) networks, measured from the content delay index (CDI), which is formulated based on the latency involved in the delivery of contents requested by ground-based mobile users. The mobile users use the cellular devices to raise the content requests. Additionally, multiple UAVs act as aerial base stations to deliver the requested contents to ground-based mobile users. The proposed work aims to maximize the QoE. Additionally, the existing works do not consider the necessity of creating groups of ground-based mobile users based on geographic locations in order to provide contents seamlessly. Therefore, we construct an optimization problem to maximize the average CDI, which is proved as an NP-hard problem. Further, multiple UAVs incur the challenges of placing the UAVs in 3D space over the distributed ground-based mobile users. To solve this NP-hard problem, we propose QoE analysis in cache-enabled multi-UAV networks (QMUN). It functions in three steps. First, it creates clusters for the ground-based mobile users using the K-means algorithm and finds the optimized 2D positions of all the UAVs. Second, it finds the optimized heights of all UAVs before assigning them to the individual cluster. Finally, it infuses proactive content caching and optimizes the cache policy. Therefore, the delay associated with the content delivery is minimized, which leads to the generation of high value of CDI. The results of simulation depict the usefulness of QMUN in terms of average CDI, average throughput, average end-to-end delay, and user satisfaction. Specifically, the results depict that the average CDI of each user improves by $11.04$%, $4.46$%, and $17.88$% compared to the QMUN Without Cache (QMUN-WC), Greedy, and Random Selection (RS).