Predicting Content Popularity Research Articles

Low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model

To meet the low delay requirements for data content access in industrial IoT, efficient content caching strategies need to be designed in the Mobile Edge Computing (MEC). Most existing caching policies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal order of content popularity without fully considering the graph topology of MEC servers and ignore the spatial correlation of content popularity. This paper proposes a low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model (STCC). We integrate graph convolutional neural network and gated recurrent unit to construct spatio-temporal graph convolutional model, which mines the spatio-temporal correlation features of content popularity and make effective predictions on the MEC server graph topology constructed by proximity and semantic relations. Furthermore, we use hierarchical clustering to classify MEC servers into collaborative domains and design a low-complexity heuristic collaborative caching content placement algorithm to minimize the average access delay. Compared with the existing MEC server caching strategy, simulation experiments show that STCC has a better prediction effect of content popularity, and achieves better performance in the two indicators of the cache hit ratio and average access delay.

Controller-driven vector autoregression model for predicting content popularity in programmable named data networking devices.

Named Data Networking (NDN) has emerged as a promising network architecture for content delivery in edge infrastructures, primarily due to its name-based routing and integrated in-network caching. Despite these advantages, sub-optimal performance often results from the decentralized decision-making processes of caching devices. This article introduces a paradigm shift by implementing a Software Defined Networking (SDN) controller to optimize the placement of highly popular content in NDN nodes. The optimization process considers critical networking factors, including network congestion, security, topology modification, and flowrules alterations, which are essential for shaping content caching strategies. The article presents a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn). Employing a multi-variant vector autoregression (VAR) model driven by an SDN controller, PaCPn periodically updates content popularity based on time-series data, including 'request rates' and 'past popularity'. It also introduces a controller-driven heuristic algorithm that evaluates the proximity of caching points to consumers, considering factors such as 'distance cost,' 'delivery time,' and the specific 'status of the requested content'. PaCPn utilizes customized DATA named packets to ensure the source stores content with a valid residual freshness period while preventing intermediate nodes from caching it. The experimental results demonstrate significant improvements achieved by the proposed technique PaCPn compared to existing schemes. Specifically, the technique enhances cache hit rates by 20% across various metrics, including cache size, Zipf parameter, and exchanged traffic within edge infrastructure. Moreover, it reduces content retrieval delays by 28%, considering metrics such as cache capacity, the number of consumers, and network throughput. This research advances NDN content caching and offers potential optimizations for edge infrastructures.

Predictive edge caching through deep mining of sequential patterns in user content retrievals

Edge caching plays an increasingly important role in boosting user content retrieval performance while reducing redundant network traffic. The effectiveness of caching ultimately hinges on the accuracy of predicting content popularity in the near future. However, at the network edge, content popularity can be extremely dynamic due to diverse user content retrieval behaviors and the low-degree of user multiplexing. It is challenging for the traditional reactive caching systems to keep up with the dynamic content popularity patterns. In this paper, we propose a novel Predictive Edge Caching (PEC) system that predicts the future content popularity using fine-grained learning models that mine sequential patterns in user content retrieval behaviors, and opportunistically prefetches contents predicted to be popular in the near future using idle network bandwidth. Through extensive experiments driven by real content retrieval traces, we demonstrate that PEC can adapt to highly dynamic content popularity at network edge, and significantly improve cache hit ratio and reduce user content retrieval latency over the state-of-art caching policies. More broadly, our study demonstrates that edge caching performance can be boosted by deep mining of user content retrieval behaviors.

Deep Reinforcement Learning for Reactive Content Caching With Predicted Content Popularity in Three-Tier Wireless Networks

With the explosive growth of micro-video applications, the mobile traffic generated by retrieving a few user-generated micro-videos has brought a massive burden to backhaul links and backbone networks. Unlike other types of videos, user-generated micro-videos are typically requested by a large number of users within an extremely short period after their release. Therefore, it is crucial to predict the content popularity and make caching decision for the newly requested content timely. To predict content popularity in different locations, the request probabilities of the content in different locations are translated into rating scores. Then, a recommendation system-based prediction model is designed to predict rating scores of the newly requested content in different locations. To increase caching diversity and realize vertically collaboration in a three-tier wireless network, a deep reinforcement learning-based reactive content caching strategy is proposed to make caching decision for the newly requested content. The main goal is to obtain a higher caching gain at a lower caching space cost. To evaluate the caching performance, a new metric, cache benefit rate, is defined as the download latency reduction brought by each bit of cache. Compared with matrix factorization, the performance of prediction model can be increased by 23.98% and 12.44% in terms of mean absolute error and root mean square error, respectively. Extensive simulations demonstrate that the proposed reactive caching strategy outperforms other caching strategies under different system parameters in terms of the cache hit rate, average download time, and cache benefit rate.

Collaborative Caching in Edge Computing via Federated Learning and Deep Reinforcement Learning

By deploying resources in the vicinity of users, edge caching can substantially reduce the latency for users to retrieve content and relieve the pressure on the backbone network. Due to the capacity limitation of caching and the dynamic nature of user requests, how to allocate caching resources reasonably must be considered. Some edge caching studies improve network performance by predicting content popularity and actively caching the most popular content, thereby ignoring the privacy and security issues caused by the need to collect user information at the central unit. To this end, a collaborative caching strategy based on federated learning is proposed. First, federated learning is used to make distributed predictions of the preferences of users in the nodes to develop an effective content caching policy. Then, the problem of allocating caching resources to optimize the cost of video providers is formulated as a Markov decision process, and a reinforcement learning method is used to optimize the caching decisions. Compared with several basic caching strategies in terms of cache hit rate, transmission delay, and cost, the simulation results show that the proposed content caching strategy reduces the cost of video providers, and has higher cache hit rate and lower average transmission delay.

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%.

Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks

Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

Federated Learning-Based Content Popularity Prediction in Fog Radio Access Networks

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated learning</i> (FL). Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters by using the stochastic variance reduced gradient (SVRG) algorithm. Finally, FL based model integration is proposed to learn the global popularity prediction model based on local models using the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only can predict content popularity accurately, but also can significantly reduce computational complexity. Moreover, we theoretically analyze the convergence bound of our proposed FL based model integration algorithm. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to existing policies.

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.

A Novel Cooperative Cache Policy for Wireless Networks

Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.

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.

Improving content popularity prediction with k-means clustering and deep-belief networks

User-Generated Content (UGC) is turning into the predominant type of internet traffic. Content popularity prediction plays a pivotal role in managing this large-scale traffic. As a result, popularity prediction is increasingly becoming an important area of research in computer networking. Generally, popularity prediction methods are classified into two groups, namely, feature-driven and early-stage. While feature-driven methods predict content popularity before publication, early-stage methods monitor early content popularities to forecast the future. Many papers have shown that early-stage popularity prediction performs better than feature-driven methods. In this paper, we improve the performance of early-stage popularity prediction by first, classifying the data into several clusters using k-means clustering with Pearson correlation distance, and then, training a Deep-Belief Network (DBN) for each cluster. We evaluate our method using a dataset of YouTube videos and show that using a generative model such as DBN for time series prediction significantly improves the performance. Numerical results indicate that our proposed method outperforms other state-of-the-art methods by reducing Mean Absolute Percentage Error (MAPE) and mean Relative Square Error (mRSE) by up to 47.86% and 25.18%.

Multi-Head Attention Based Popularity Prediction Caching in Social Content-Centric Networking With Mobile Edge Computing

With the rapid growth of social network traffic, the design of an efficient caching strategy is crucial in the social content-centric network (SocialCCN). In order to design a more comprehensive popularity prediction caching strategy, in this letter, we proposed a novel architecture that integrates mobile edge computing (MEC) in SocialCCN (MeSoCCN) and proposed multi-head attention based popularity prediction caching strategy in MeSoCCN. Firstly, we proposed a multi-head attention based popularity prediction model (MAPP) that considers multi-dimensional features including history and future popularity, social relationships, and geographic location to predict content popularity. Then, we design a caching strategy based on the prediction results of MAPP. The simulation results show that the proposed MAPP model achieves lower predictive error and the proposed predictive caching strategy improves cache hit rate and reduces hop redundancy in the network.

Network Friendly Recommendations: Optimizing for Long Viewing Sessions

Caching algorithms try to predict content popularity, and place the content closer to the users. Additionally, nowadays requests are increasingly driven by recommendation systems (RS). These important trends, point to the following: \emph{make RSs favor locally cached content}, this way operators reduce network costs, and users get better streaming rates. Nevertheless, this process should preserve the quality of the recommendations (QoR). In this work, we propose a Markov Chain model for a stochastic, recommendation-driven \emph{sequence} of requests, and formulate the problem of selecting high quality recommendations that minimize the network cost \emph{in the long run}. While the original optimization problem is non-convex, it can be convexified through a series of transformations. Moreover, we extend our framework for users who show preference in some positions of the recommendations' list. To our best knowledge, this is the first work to provide an optimal polynomial-time algorithm for these problems. Finally, testing our algorithms on real datasets suggests significant potential, e.g., $2\times$ improvement compared to baseline recommendations, and 80\% compared to a greedy network-friendly-RS (which optimizes the cost for I.I.D. requests), while preserving at least 90\% of the original QoR. Finally, we show that taking position preference into account leads to additional performance gains.

Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

Proactive Caching and Bandwidth Allocation in Heterogenous Networks by Learning From Historical Numbers of Requests

Proactive caching at base stations (BSs) has been shown promising in offloading traffic, where most priori works consider full frequency reuse among cells and assume known file popularity. To facilitate proactive caching, recent works either adopt linear models or shallow neural networks to predict popularity, without explaining the rationale. In this paper, we consider cache-enabled multi-tier heterogenous networks. To show if full frequency reuse is superior, we consider underlay and overlay modes with random bandwidth allocation for interference management. After optimizing the caching policy and bandwidth allocation to maximize successful offloading probability, we show that the overlay mode outperforms the underlay mode. To confirm that complex non-linear prediction models are unnecessary for proactive caching when using the historical numbers of requests, we employ several linear and non-linear models to predict content popularity and user density, using MovieLens and Youku datasets. To interpret why the caching performance achieved by the optimized policies with the information predicted by the linear models is close to that using non-linear models, we prove that deterministic popularity with typical profiles can be predicted with linear models. Then, we show that most popular files are with these profiles in the real dataset.

Popularity prediction caching based on logistic regression in vehicular content centric networks

To improve the network performance caused by mobility and sporadic connectivity in the vehicular network, vehicular content centric network (VCCN) is proposed by applying CCN into the vehicular network. The open in-network caching of CCN makes nodes cache contents cooperatively to facilitate information access. To improve the network performance such as access delay and hit ratio, road side units (RSUs) should try to cache more popular contents and provide better service for mobile users. This paper aims to propose a novel cache replacement policy - popularity prediction content caching (PPCC) for VCCN. In PPCC, we incorporate the future popularity of contents into our decision making. By learning the popularity of contents, we propose a cache replacement method based on logistic regression for RSUs in order to store those frequently access contents. The input data are related to the inherent characters of the received interests and the output is the predicted content popularity which guarantees that only popular contents are cached in the network infrastructures (i.e., RSUs). Simulation evaluations demonstrate that our scheme is very effective with higher cache hit, lower access latency and higher caching efficiency compared to other state-of-the-art schemes.

Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

Feedforward Neural Networks for Caching

We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.

Deep Q-Learning-Based Content Caching With Update Strategy for Fog Radio Access Networks

In order to improve the edge caching efficiency of the fog radio access network (F-RAN), this paper put forward a distributed deep Q-learning-based content caching scheme based on user preference prediction and content popularity prediction. Given that the constraint that the storage capacity of each device is limited, and the optimization problem is formulated so as to maximize the caching hit rate. Specifically, by taking users’ selfishness into consideration, user preference is predicted in an offline manner by applying popular topic models. Then, the online predicted content popularity is achieved by combining the network topology relationship together with the obtained user preference. Finally, with the predicted user preference and content popularity, the deep Q-learning network (DQN)-based content caching algorithm is proposed to achieve the optimal content caching strategy. Moreover, we further present a content update policy with user preference and content popularity prediction, so that the proposed algorithm can handle the variations of contents popularity in a timely manner. Simulation results demonstrate that the proposed scheme achieves better caching hit rate compared with existing algorithms.