Caching In Networks Research Articles

Community Detection and Attention-Weighted Federated Learning Based Proactive Edge Caching for D2D-Assisted Wireless Networks

This work investigates proactive edge caching for D2D-assisted wireless networks, where user equipments (UEs) can be selected as caching nodes to assist content delivery. The objective of this work is to achieve a trade-off between the cost for providing caching services and the content transmission latency. Doing so, there are two challenges: 1) Which UEs can be selected as caching nodes; 2) How to place contents on these selected UEs without user’s privacy disclosure. To address these, a novel community detection and attention-weighted federated learning based proactive edge caching (CAFLPC) strategy is proposed. In the strategy, we first group UEs into different communities based on both the mobility and social properties of UEs, and then select important users (IUs) as caching nodes for each community by considering the social importance of UEs. To determine how to place the popular contents in these selected IUs, an attention-weighted federated learning (AWFL) based content popularity prediction framework is proposed. It integrates the attention-weighted federated learning with Bidirectional Long Short Term Memory Network (AWFL_BiLSTM) to achieve a higher content popularity prediction accuracy while protecting user’s privacy. Considering the imbalance of UEs’ active levels and local computing capacities, an attention-weighted aggregation mechanism is proposed to improve the training efficiency and prediction accuracy. Simulations results show that the proposed CAFLPC strategy outperforms the compared existing caching strategies at about 2.2%-35.1% in terms of the transmission latency reduced by per unit cost.

Cooperative Task Offloading and Service Caching for Digital Twin Edge Networks: A Graph Attention Multi-Agent Reinforcement Learning Approach

Cooperative Task Offloading and Service Caching for Digital Twin Edge Networks: A Graph Attention Multi-Agent Reinforcement Learning Approach

Boosting in-transit entertainment: deep reinforcement learning for intelligent multimedia caching in bus networks

Boosting in-transit entertainment: deep reinforcement learning for intelligent multimedia caching in bus networks

Joint mixed-timescale optimization of content caching and delivery policy in NOMA-based vehicular networks

Recently, the development of Internet of Vehicles (IoV) and the increasing popularity of video applications have led to the fast-growing in-car video demand causing numerous challenges in wireless networks. Pre-caching and non-orthogonal multiple access (NOMA) have been regarded as two effective techniques to alleviate the mentioned challenge. In this paper, we propose a cache-aided cooperative transmission to maximize the quality of service (QoS) in the NOMA-based vehicular network. A QoS-oriented joint optimization problem is formulated, which incorporates power allocation, content caching, and delivery strategy. Considering, on the one hand, the slow update rate of cache content and, on the other hand, frequent handovers of vehicles between different transmitters, a mixed-timescale optimization is proposed where the serving cache is updated in a long-term phase, while content delivery and power allocation are optimized in a short-term phase. In the proposed approach, content caching is determined based on future user requests, vehicle tracking, and other delivery information. To make this possible, we leverage a substantial number of stochastic samples to approximate content caching in the long-term caching phase. Due to the NOMA-based transmission and integral variables, the setting leads to a Mixed Integer Non-Linear Programming (MINLP) problem, which is NP-hard. To solve this problem, an iterative method based on sample average approximation (SAA) and Successive Convex Approximation (SCA) is applied. Simulations demonstrate that the proposed algorithm can achieve better QoS than other recently proposed transmission schemes.

Revving up VNDN: Efficient caching and forwarding by expanding content popularity perspective and mobility

Network caching in Vehicular Named Data Networks (VNDN) has the potential to support latency-sensitive services, though given the massive amount of content generated by vehicles in a VNDN, it is challenging to cache sufficiently diverse content across the network for an acceptable hit ratio. Redundant interest transmissions can negatively impact network overhead, wasting network resources when retrieving duplicate content. In the literature, a caching decision is typically based on ongoing popularity, computed from a vehicle’s experience with requests received for individual content. This leads to each vehicle caching the most popular content. Instead, we group content into different categories. All content in a popular category of vehicular service (e.g., tourist information category) is cached equally, not just the most popular content within a category (e.g., information relating to a particular museum). This increases content diversity in the cache. Further, we posit that a service requester from a particular location at a specific time would be interested in the same category of service/application as other users from the same location/time. Leveraging this observation, we consider both forthcoming spatial/temporal and ongoing popularity of vehicular services when ranking their popularity. We also address network overhead with a mobility model that captures the communication duration between vehicles. This parameter, which has been overlooked in existing literature, enables the selection of an appropriate forwarder for content requests. We use ndnSIM VNDN with a mobility trace of Luxembourg city for simulation. Compared to an approach where individual content-wise popularity is computed, our scheme shows more than 100% improvement in cache hit ratio where the number of contents in the network varied between 104 and 105. Simultaneously, we also enhanced the diversity of content caching, while considering the trade-off between cache hit ratio and network overhead.

Ultra dense satellite-enabled 6G networks: Resource optimization and interference management

With the evolution of the sixth generation (6G) mobile communication technology, ample attention has gone to the integrated terrestrial-satellite networks. This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture. Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced. Satellite mobile edge computing (SMEC) with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks. Furthermore, a scheme for interference management is presented, involving quality-of-service (QoS) and co-tier/cross-tier interference constraints. The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

CLoSER: Video caching in small-cell edge networks with local content sharing

We consider the problem of video caching in an edge network consisting of a set of small-cell base stations (SBS) that can share content among themselves over a high-capacity short-delay local network, or fetch the videos from a remote server over a long-delay connection. Even though the problem of minimizing the overall video playout delay in our framework is NP-hard, we develop CLoSER, an algorithm that can efficiently compute a solution that is close to the optimal, where the degree of sub-optimality depends on the worst case video-to-cache size ratio. In comparison with related prior work on video caching and streaming, CLoSER specifically focuses on the benefits of local sharing of the initial portion of the video content in reducing the video playout delay, and provides strong optimality guarantees with a low-complexity algorithm. We extend CLoSER to an online setting where the video popularities are not known a priori but are estimated over time through a limited amount of periodic information sharing between SBSs. With such online video popularity estimation, a distributed implementation of CLoSER requires zero explicit coordination between the SBSs and runs in O(NK+KlogK) time, where N is the number of SBSs (caches) and K the maximum number of videos. We carry out simulations using YouTube and Netflix video request traces, as well as synthesized traces with the same marginal distributions as the traces but varying degree of temporal correlations, and demonstrate that our algorithm uses the SBS caches effectively to reduce the video delivery delay and conserve the remote server’s bandwidth. We also show that it outperforms two other reference caching methods adapted to our system setting, over a wide range of remote-to-local bandwidth ratios. Further, we show how CLoSER extends to the scenario where each video may need to be cached in multiple bit-rates, as in Available Bit Rate (ABR) streaming.

Performance Analysis of D2D Cache Network for Total Hit Probability and Energy Consumption

Performance Analysis of D2D Cache Network for Total Hit Probability and Energy Consumption

NDN-Based Coded Caching Strategy for Satellite Networks

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

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

Scheduling for Multi-Cell Caching: Hit Maximization Subject to Age of Information

In this paper, we investigate how to schedule content updates for multi-cell caching networks. We model a hit ratio maximization problem subject to age of information (AoI), and prove the NP-hardness of the problem. For problem solving, we propose a scalable and effective algorithm based on repeated cutting plane (RCP), in which a cutting plane algorithm and a rounding algorithm are implemented iteratively. In addition, we derive a bound that serves as a benchmark of any suboptimal solution. Performance evaluations show that for RCP, the performance gap to optimum is less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.5\%$</tex-math></inline-formula> .

Mobility-aware caching strategy in an SDN-based cooperative caching network

The upsurge of mobile multimedia traffic puts a heavy burden on the cellular network, and wireless caching has emerged as a powerful technique to overcome the backhaul bottleneck and alleviate the network burden. However, most previous works ignored user mobility, thus not reaping the caching gain from user mobility and having limited practical applications. In this paper, a mobility-aware caching strategy for the software-defined network (SDN)-based network is studied. Firstly, since typical mobile user (MU) has multiple opportunities to connect with the nearby MUs and Small-base stations (SBSs), the contact times between MUs as well as between MU and SBSs are derived as the Poisson distribution and Gamma distribution. Secondly, we propose a two-tier cooperative caching strategy, where SBSs cache the rateless Fountain code encoded video blocks probabilistically and nonrepeatedly while MUs just store the whole encoded video received last time. The corresponding four-stage transmission process is analyzed, where the key intermediate step is the derivation of the service and the failing probabilities of each transmission manner. Finally, we derive the successful offloading rate and average data offloading ratio (ADOR) as performance metrics. A system optimization problem based on ADOR is formulated, and two solutions are proposed, namely, derivative-based solution (DB-Solution) and long-tail distribution approximation (LTD-Approximation). Simulation results demonstrate that the effectiveness of LTD-Approximation is similar to the DB-Solution, and the proposed caching strategy can achieve quasi-optimal performance compared with other contrast schemes.

Joint Caching and Routing in Cache Networks With Arbitrary Topology

In-network caching and flexible routing are two of the most celebrated advantages of next generation network infrastructures. Yet few solutions are available for jointly optimizing caching and routing that provide performance guarantees for networks with arbitrary topology. We take a holistic approach towards this fundamental problem by analyzing its complexity in all the cases and developing polynomial-time algorithms with approximation guarantees in important special cases. We also reveal the fundamental challenge in achieving guaranteed approximation in the general case and propose an alternating optimization algorithm with good empirical performance and fast convergence. Our algorithms have demonstrated superior performance in both routing cost and congestion compared to the state-of-the-art solutions in evaluations based on real topology and request traces.

Federated Online Learning Aided Multi-Objective Proactive Caching in Heterogeneous Edge Networks

To address the drastic increase in multimedia traffic volume, mobile edge caching (MEC) has been exploited to reduce redundant data transmissions by equipping computation and storage capacity at the edge network. Previous works on learning-based caching problems often only concern pre-storing popular contents to satisfy users’ demands. In this work, we investigate the cache strategy design problem with two possibly conflicting objectives, namely, cache hit and cache profit, in heterogeneous multi-MEC server networks when content profiles are unknown. We then formulate this multi-objective caching problem as a Multi-agent Multi-objective Combinatorial Multi-Armed bandit (MMC-MAB) problem and propose a two-step caching framework that estimates content properties first and then optimizes cache placement. Specifically, to accommodate the system heterogeneity in estimation, we utilize an adaptive federated learning-based estimation to approach the unknown content popularity and profit profiles, which wisely use both local and external observations by adjusting the mixing factors. To address the multiple objective optimizations, we propose two effective methods, based on individual dominance and combinatorial dominance, to achieve adequate Pareto-optimal cache placement. Both theoretical results and comprehensive experiments clearly validate the effectiveness and efficiency of our proposed approaches.

Performance Analysis and Optimization for Layer-Based Scalable Video Caching in 6G Networks

Scalable video caching is a promising technique to alleviate backbone traffic in sixth generation (6G) networks, and to serve users with video quality that adapts to varying channel conditions. In this paper, we develop a layer-based scalable video caching technique with non-orthogonal transmission by taking advantage of the layer feature in the scalable video. In addition, the impact of different serving base station selection algorithms is investigated. Our results indicate that both the caching placement design and transmission scheme design dominate the caching performance. To evaluate the interplay of these two policies, a tractable metric of Caching Aided Data Rate (CADR) is characterized and maximized by jointly optimizing the aforementioned two policies. Together with extensive Monte Carlo simulations, numerical results are also evaluated in this paper, demonstrating that the proposed Layer-based video Caching scheme with Non-Orthogonal Transmission (LCNOT) can achieve higher CADR performance than other baseline schemes.

Secretive Coded Caching With Nonuniform Demands in Fog Radio Access Networks

In this letter, we investigate the problem of secretive coded caching in fog radio access networks (F-RANs), aiming at minimizing the average backhaul rate under nonuniform file popularity distribution. Firstly, a two-layer placement scheme which jointly considers secrecy constraints and cache resources in the network is designed. Next, a two-stage delivery scheme which utilizes the network architecture characteristics and exploits the redundancy in user demands is designed. By integrating the above two schemes, a novel secretive coded caching scheme is proposed. Finally, we derive the expression of the average backhaul rate and evaluate the performance of the proposed scheme via simulations. Simulation results show that our proposed scheme outperforms the state of the art in terms of backhaul rate reduction.

Latency Minimization for Advanced RSMA-Enabled Wireless Caching Networks

The collaboration between wireless edge caching and rate splitting multiple access (RSMA) can significantly reduce the perceived latency of users. In this letter, an advanced RSMA transmission scheme is proposed with the consideration of users’ content requests. In traditional RSMA, multiple replicas of the same messages are transmitted when users request the same contents, which cause the redundant interference. The advanced RSMA is proposed to reduce such redundant interference. Based on the newly designed transmission paradigm, we investigate the joint cache placement and RSMA resource management problem, aiming to minimize the average latency of all users. The formulated problem is a non-convex mixed-integer nonlinear programming (MINLP) problem. By leveraging its structural features, we decompose the original minimization problem into two types of subproblems, i.e., the long-term caching decision problem and the short-term RSMA resource allocation problem. The caching decision problem is tackled by the dynamic programming approach. Whilst the RSMA resource allocation problem is solved by an alternating optimization method with one-dimensional search. Extensive simulation results validate the superiority of our proposed scheme compared to various benchmark strategies.

Cache Placement in an NDN-Based LEO Satellite Network Constellation

The efforts to replace the successful, albeit aging, TCP/IP Internet architecture with a better suited one have driving research interest to information-centric alternatives. The Named Data Networking (NDN) architecture is probably one of the main contenders to become the network layer of the future Internet thanks to its inbuilt support for mobility, in-network caching, security and, in general, for being better adapted to the needs of current network applications. At the same time, massive satellite constellations are currently being deployed in low Earth orbits (LEO) to provide a backend for network connectivity. It is expected that, very soon, these constellations will function as proper networks thanks to inter-satellite communication links. These new satellite networks will be able to benefit from their greenfield status and the new network architectures. In this paper we analyze how to deploy the network caches of an NDN-based LEO satellite network. In particular, we show how we can jointly select the most appropriate caching nodes for each piece of content and how to forward data across the constellation in two simple alternative ways. Performance results show that the caching and forwarding strategies proposed reduce path lengths up to a third with just a few caching nodes while, simultaneously, helping to spread the load along the network.

Recommend what to cache: a simple self-supervised graph-based recommendation framework for edge caching networks

Deep Learning-based edge caching networks can accurately infer what to cache based on a user's historical content requests, thereby significantly relieving the burden of the backbone networks. However, the cold-start problem inherent in deep learning may limit the performance of history-based caching strategies. Due to the mobile and dynamic nature of wireless networks, base stations often lack sufficient data to accurately estimate the user's demands and cache the possible requested data. In this context, we adopt self-supervised learning (SSL) into the caching strategies and propose a Simple Self-supervised Graph-based Recommendation framework for edge caching networks (SimSGR). Specifically, we propose two new network layers: the Mixing layer and the Conversion layer. The former replaces the data augmentation of the SSL paradigm to avoid destroying the semantic loss, while the latter greatly simplifies the loss function, which helps to lighten the model structure and facilitates deployment on edge caching networks. Simulation results show that our model outperforms baseline algorithms that are sensitive to augmentation hyper-parameters, particularly when trained in a cold-start environment.

Maximizing the Capacity of Edge Networks with Multicasting

Edge networks employ local computing and caching resources to process data, thus alleviating the bandwidth pressure on backbone networks and improve users’ quality of experience. System capacity is one of the key metrics to evaluate the performance of edge networks. However, maximizing system capacity in edge scenarios faces challenges due to the dynamical user sessions and the changing content popularity. This study reports on the influence of multicast communication on the capacity of edge caching networks. When large amounts of content are requested simultaneously or over a short period, one-to-one unicast transmission will consume copious network resources due to repetitious transmission. To solve this problem, this study used the one-to-many multicast scheme to realize cooperative transmission between edge servers. First, multiple copies of the content are distributed to multiple small base stations (SBSs) based on the content’s popularity and the SBSs’ cache sizes. Second, a multicast tree is constructed by using, as the root node, the SBS that stores the content. Third, the content is transmitted along the path of the multicast tree to each end-user. Finally, a simulation platform is constructed to analyze the performance of the two transmission schemes. The results of simulating on edge caching networks show that multicast communication responds well to users’ requests even when the requested content requires sudden transmission, is highly popular or is requested often within short time. This system’s capacity has been significantly improved compared to the classical methods.