Collaborative Caching Research Articles

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Social-Aware Assisted Edge Collaborative Caching Based on Deep Reinforcement Learning Joint With Digital Twin Network in Internet of Vehicles

Social-Aware Assisted Edge Collaborative Caching Based on Deep Reinforcement Learning Joint With Digital Twin Network in Internet of Vehicles

Optimization model for vehicular network data queries in edge environments

As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency.

Social-Aware Collaborative Caching in Edge-User Networks: A Joint Multi-Graph Approach

Social-Aware Collaborative Caching in Edge-User Networks: A Joint Multi-Graph Approach

Toward High-Quality Low-Latency 360° Video Streaming With Edge–Client Collaborative Caching and Super-Resolution

Toward High-Quality Low-Latency 360° Video Streaming With Edge–Client Collaborative Caching and Super-Resolution

Adaptive Partitioning and Placement for Two-Layer Collaborative Caching in Mobile Edge Computing Networks

Adaptive Partitioning and Placement for Two-Layer Collaborative Caching in Mobile Edge Computing Networks

Multiagent Federated Deep-Reinforcement-Learning-Based Collaborative Caching Strategy for Vehicular Edge Networks

Multiagent Federated Deep-Reinforcement-Learning-Based Collaborative Caching Strategy for Vehicular Edge Networks

A collaborative cache allocation strategy for performance and link cost in mobile edge computing

A collaborative cache allocation strategy for performance and link cost in mobile edge computing

Collaborative Video Caching in the Edge Network using Deep Reinforcement Learning

With the enormous growth in mobile data traffic over the 5G environment, Adaptive BitRate (ABR) video streaming has become a challenging problem. Recent advances in Mobile Edge Computing (MEC) technology make it feasible to use Base Stations (BSs) intelligently by network caching, popularity-based video streaming, and more. Additional computing resources on the edge node offer an opportunity to reduce network traffic on the backhaul links during peak traffic hours. More recently, it has been found in the literature that collaborative caching strategies between neighbouring BSs (i.e., MEC servers) make it more efficient to reduce backhaul traffic and network congestion and thus improve the viewer experience substantially. In this work, we propose a Reinforcement Learning (RL)–based collaborative caching mechanism in which the edge servers cooperate to serve the requested content from the end-users. Specifically, this research aims to improve the overall cache hit rate at the MEC, where the edge servers are clustered based on their geographic locations. This task is modelled as a multi-objective optimization problem and solved using an RL framework. In addition, a novel cache admission and eviction policy is defined by calculating the priority score of video segments in the clustered MEC mesh network.

DRL-Based Resource Allocation and Trajectory Planning for NOMA-Enabled Multi-UAV Collaborative Caching 6G Network

DRL-Based Resource Allocation and Trajectory Planning for NOMA-Enabled Multi-UAV Collaborative Caching 6G Network

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.

Decentralized Collaborative Caching in Ultra-Dense Networks

Decentralized Collaborative Caching in Ultra-Dense Networks

Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks

Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks

Edge collaborative caching solution based on improved NSGA II algorithm in Internet of Vehicles

With the rapid development of the Internet of Vehicles technology and the continuous advancement of intelligent transportation, vehicle network traffic is growing exponentially. Traditional caching solutions transmit content through the core network, causing significant latency. A vehicle-cloud collaborative edge caching network architecture was proposed to address this type of problem. Using this architecture, both the on-board terminals and cloud/edge servers could provide computing services. A communication model, caching model, latency model, energy consumption model, load model and multi-objective optimization problem model were designed. The edge collaborative caching solution based on the improved NSGA II algorithm was proposed. By caching some services to edge nodes and nearby vehicles, reducing content access latency and improving resource utilization. The results indicate that the caching solution outperforms the comparison scheme in terms of the comprehensive costs of latency, energy consumption, and load balancing in simulation experiments. It can meet the caching requirements of low latency and low energy consumption for in-vehicle applications, laying a solid foundation for achieving more efficient and reliable connected vehicles and autonomous driving.

Efficient V2V Communications by Clustering-Based Collaborative Caching

Vehicle-to-vehicle (V2V) communication plays an important role in enabling autonomous driving. However, when multiple vehicles request the same content, like road conditions, delivering it individually by V2V communication can significantly increase traffic volume, potentially causing congestion in the wireless channel. To address this issue, Content-Centric Network (CCN) technology is applied to V2V communication, which improves communication efficiency by exploiting content cached at vehicles. However, previous methods faced the following challenges: (i) vehicles could not use content stored in nearby vehicles outside the communication path, and (ii) redundant caching of the same content occurred at nearby vehicles. To tackle these challenges, this paper proposes a collaborative caching method in which vehicles are grouped into clusters and each cluster has a designated head responsible for managing caches across all vehicles within the cluster. In this way, this method enables vehicles to use the content cached at adjacent vehicles that are not directly on a communication path. In addition, it eliminates redundant caches, allowing a more diverse range of content storage. Extensive simulation results demonstrate that the proposed approach effectively reduces content delivery latency by 33% compared to the method using clusters without cooperative caching and by 19% compared to the ECV+ method.

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

Energy efficiency of cache collaboration in core content distribution networks

With escalating demands for high-definition video, cache collaboration allows neighbor nodes to share locally stored content in order to reduce download traffic. High energy consumption associated with content delivery remains a concern for Content Distribution Networks (CDNs). Therefore, this paper proposes cluster-based collaborative caching in a core network employing IP over WDM. The aim is to allow sets of core caches to fully share content while minimizing power. A Mixed Integer Linear Programming (MILP) model is used to form energy-efficient cache clusters. The energy consumption of the network is evaluated under different cluster sizes to find the optimum size that minimizes energy. To evaluate the influence of content popularity distribution, a heavy-tailed Zipf distribution and an Equal popularity distribution are evaluated. In addition, the work investigates the influence of downlink traffic behavior and power consumption parameters on optimum cluster sizes. Attained results reveal that maximum savings in energy consumption introduced by cluster-based collaborative caching are up to 34.3% and 21.8% under the Zipf and Equal distribution, respectively. Cache collaboration is not recommended when all core nodes contain fully replicated content servers. Results also show that power consumption parameters do not influence cluster formation. It is recommended keeping cache collaboration in the core network simple, so as to reduce intra-cluster communication.

Collaborative Caching and Reconfigurable Intelligent Surface for the Sub-THz Mobile System

Collaborative Caching and Reconfigurable Intelligent Surface for the Sub-THz Mobile System

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.

Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO

Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO