Popular Files Research Articles

Learning-based cooperative content caching and sharing for multi-layer vehicular networks

Caching and sharing the content files is critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.

Interference-aware scheme to improve distributed caching in cellular networks via D2D underlay communications

Underlay Device-to-Device (D2D) communications is a promising networking technology intended to boost thespectral efficiency of future cellular networks, including 5G and beyond. When used for distributed caching, where cellulardevices store popular files for direct exchange later with other devices away from the cellular infrastructure, the technologybears more fruits such as enhancing throughput, reducing latency and offloading the infrastructure. However, due to theirnon-orthogonality, underlay D2D communications can result in excessive interference to the cellular user. To avoid thisproblem, the present article proposes a scheme with two interference-reduction elements: a guard zone intended to allowD2D communications only for devices far enough from the base station (BS), and a pairing strategy intended to allow D2Dpairing for only devices that are close enough to each other. We assess the performance of the scheme using a stochasticgeometry (SG) model, through which we characterize the coverage probability of the cellular user. This probability is aprincipal indicator of maintaining the quality of service (QoS) of the cellular user and of enabling successful caching for theD2D user. We introduce in the process a novel empirical technique which, given a desired level of interference, identifiesan upper bound for the distance between two devices to be paired without exceeding that level. We finally validate theanalytical findings obtained from the model by intensive simulation to ensure the correctness of both the model and thescheme performance. A salient feature of the scheme is that it requires for its implementation no software or hardwaremodification in the device

Intelligent Caching for Vehicular Dew Computing in Poor Network Connectivity Environments

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

Decentralized Caching Under Nonuniform File Popularity and Size: Memory-Rate Tradeoff Characterization

This paper aims to characterize the memory-rate tradeoff for decentralized caching under nonuniform file popularity and size. We consider a recently proposed decentralized modified coded caching scheme (D-MCCS) and formulate the cache placement optimization problem to minimize the average rate for the D-MCCS. To solve this challenging non-convex optimization problem, we first propose a successive Geometric Programming (GP) approximation algorithm, which guarantees convergence to a stationary point but has high computational complexity. Next, we develop a low-complexity file-group-based approach, where we propose a popularity-first and size-aware (PF-SA) cache placement strategy to partition files into two groups, taking into account the nonuniformity in file popularity and size. Both algorithms do not require the knowledge of active users beforehand for cache placement. Numerical results show that they perform very closely to each other. We further develop a lower bound for decentralized caching under nonuniform file popularity and size as a non-convex optimization problem and solved it using a similar successive GP approximation algorithm. We show that the D-MCCS with the optimized cache placement attains this lower bound when no more than two active users request files at a time. The same is true for files with uniform size but nonuniform popularity and the optimal cache placement being symmetric among files. In these cases, the optimized D-MCCS characterizes the exact memory-rate tradeoff for decentralized caching. For general cases, our numerical results show that the average rate achieved by the optimized D-MCCS is very close to the lower bound.

Analysis and Optimization of Cache-Enabled mmWave HetNets With Integrated Access and Backhaul

In millimeter wave heterogeneous networks with integrated access and backhaul (mABHetNets), a considerable part of spectrum resources are occupied by the backhaul link, which limits the performance of the access link. In order to overcome such backhaul “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectrum occupancy</i> ”, we introduce cache into mABHetNets. Caching popular files at small base stations (SBSs) can offload the backhaul traffic and transfer spectrum from the backhaul link to the access link. To approach the optimal performance of cache-enabled mABHetNets, we first analyze the signal-to-interference-plus-noise ratio distribution and derive the average potential throughput (APT) expression by stochastic geometric tools. Then, based on our analytical work, we formulate a joint optimization problem of cache decision and spectrum partition to maximize the APT. Inspired by the block coordinate descent (BCD) method, we propose a joint cache decision, spectrum partition and power allocation (JCSPA) algorithm to approach the optimal solution. Numerical results show the effectiveness and enhancement of the proposed algorithm. Besides, we verify the APT under different parameters and find that the introduction of cache facilitates the transfer of backhaul spectrum to access link. Jointly deploying appropriate caching capacity at SBSs and performing specified spectrum partition can bring up about 90% APT gain in mABHetNets.

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.

Multi-Transmitter Coded Caching Networks With Transmitter-Side Knowledge of File Popularity

This work presents a new way of exploiting non-uniform file popularity in coded caching networks. Focusing on a fully-connected fully-interfering wireless setting with multiple cache-enabled transmitters and receivers, we show how non-uniform file popularity can be used very efficiently to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching. This approach is motivated by the recent discovery that, under any realistic file-size constraint, having content appear in multiple transmitters can in fact dramatically boost the speed-up factor attributed to coded caching. We formulate an optimization problem that exploits file popularity to optimize the placement of files at the transmitters. Consequently, we propose a search algorithm that solves the problem at hand while reducing the variable search space significantly. We also prove an analytical performance upper bound, which is in fact met by our algorithm in the regime of many receivers. Our work reflects the benefits of allocating higher cache redundancy to more popular files, but also reflects a law of diminishing returns where for example very popular files may in fact benefit from minimum redundancy. In the end, this work reveals that in the context of coded caching, employing multiple transmitters can be a catalyst in fully exploiting file popularity, as it avoids various asymmetry complications that appear when file popularity is used to alter the receiver-side cache placement.

Video File Allocation for Wear-Leveling in Distributed Storage Systems With Heterogeneous Solid-State-Disks (SSDs)

With the advent of new large-capacity solid-state disks (SSDs) such as quad-level-cells (QLC), SSD arrays can be effectively used in video storage systems that require large-capacity storage space. Typically, SSD manufacturers specify a drive-writes-per-day (DWPD) metric, which is the ratio of bytes written per day to the total capacity in bytes, to ensure an SSD’s specified lifetime; it is important to limit the number of write operations by considering the DWPD for each SSD. We propose a new video file allocation technique to effectively manage the heterogeneous DWPD characteristics of SSDs in distributed storage systems. To express the degree of wear-leveling for heterogeneous SSDs, we first introduce the concept of ADWD, which is the actual number of bytes written per day compared to DWPD. We then propose two algorithms for file placement and migration. The file placement algorithm places files greedily based on the bandwidth-to-space ratio (BSR) of each file and SSD to balance the bandwidth usage and storage of the SSD. The file migration algorithm moves files from overloaded to underloaded SSDs to meet bandwidth limit requirements while minimizing the overall ADWD as a result of migration, and then migrates additional popular files to improve SSD bandwidth utilization. To use these algorithms in actual distributed file systems, we implemented a suite of tools for file placement and migration in the Hadoop distributed file system (HDFS). Experimental results show that the proposed algorithm reduces the mean of ADWD by 35.44% and its standard deviation by 69.78% compared to the benchmark methods on average.

Feedback Delay-Tolerant Proactive Caching Scheme Based on Federated Learning at the Wireless Edge

Edge caching has emerged as a promising approach to meet explosive mobile data on 6G networks. One critical issue in edge caching is file popularity prediction. The federated learning (FL) based distributed algorithm is used to predict file popularity, which solves user privacy issues in centralized algorithms. However, due to the heterogeneity of user devices, the feedback time (i.e., the update and upload time of model parameters) is various for each user, which causes the feedback delay in the framework of FL-based edge caching. In this letter, we devise a feedback delay-tolerant proactive caching scheme (FLASH) based on FL in which each user utilizes hybrid filtering on stacked autoencoders to train the prediction model locally. Experimental results indicate that FLASH can effectively handle feedback delay and outperform counterpart algorithms in cache efficiency.

A Novel Nested Coded Delivery Strategy for Coded Caching Under Nonuniform File Popularity

This letter proposes an efficient nested coded caching scheme (NCCS) under nonuniform file popularity to reduce the average delivery rate. The NCCS uses a nested coded delivery strategy to ensure the subfiles of the files being coded into the same message are of the same size, thereby eliminating zero-padding that is commonly used in coded messages for delivery and, at the same time, maximally exploiting multicasting gain. We further optimize the NCCS by jointly optimizing the cache placement and nested coded delivery strategy to minimize the average delivery rate. Numerical results show that the optimized NCCS outperforms other state-of-the-art coded caching schemes and achieves near-optimal performance.

Energy and Latency Efficient Caching in Mobile Edge Networks: Survey, Solutions, and Challenges

Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to-device (D2D) communication. In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented. An illustration of the research in cache development for wireless networks that apply intelligent and learning techniques in a specific domain in their design is presented. We summarize the challenges that face the design of caching system in MENs. Finally, some future research directions are discussed for the development of cache placement and cache access and delivery in MENs.

Optimizing Caching in a C-RAN With a Hybrid Millimeter-Wave/Microwave Fronthaul Link via Dynamic Programming

Placing selected content at the edge of the network close to the users, known as caching, can lower network latency and congestion in the fronthaul link. Unlike most works that assume a fixed or limited variation in file popularities, to better address user requests, we consider a time-varying popularity resulting in hidden-mode Markov decision processes. In fact, each mode captures environmental changes, and we optimize the fetching and dropping (of files) decisions to minimize a long-term network cost in a cloud radio access network. Importantly, the primary fronthaul link is a millimeter (mmWave) link with large capacity supported by a microwave backup link in case of blockage. Since caching decisions are coupled over time and can affect the future, we introduce a dynamic programming approach to solve the caching problem. We approximate the future cost of each cache state in each mode. To reduce the complexity of calculating the future cost, we introduce two approximation approaches and illustrate the accuracy of the approximations under different environmental conditions. Finally, our simulation results confirm the effectiveness of our proposed algorithm in finding effective caching and fetching decisions to lower the total network cost while dealing with time-varying popularities.

Memory-Rate Tradeoff for Caching With Uncoded Placement Under Nonuniform Random Demands

For a caching system with multiple users, we aim to characterize the memory-rate tradeoff for caching with uncoded cache placement, under nonuniform file popularity. Focusing on the modified coded caching scheme (MCCS) recently proposed by Yu, etal., we formulate the cache placement optimization problem for the MCCS to minimize the average delivery rate under nonuniform file popularity, restricting to a class of popularity-first placements. We then present two information-theoretic lower bounds on the average rate for caching with uncoded placement, one for general cache placements and the other restricted to the popularity-first placements. By comparing the average rate of the optimized MCCS with the lower bounds, we prove that the optimized MCCS attains the general lower bound for the two-user case, providing the exact memory-rate tradeoff. Furthermore, it attains the popularity-first-based lower bound for the case of general K users with distinct file requests. In these two cases, our results also reveal that the popularity-first placement is optimal for the MCCS, and zero-padding used in coded delivery incurs no loss of optimality. For the case of K users with redundant file requests, our analysis shows that there may exist a gap between the optimized MCCS and the lower bounds due to zero-padding. We next fully characterize the optimal popularity-first cache placement for the MCCS, which is shown to possess a simple file-grouping structure and can be computed via an efficient algorithm using closed-form expressions. Finally, we extend our study to accommodate both nonuniform file popularity and sizes, where we show that the optimized MCCS attains the lower bound for the two-user case, providing the exact memory-rate tradeoff. Numerical results show that, for general settings, the gap between the optimized MCCS and the lower bound only exists in limited cases and is very small.

Dynamic Caching for Files With Rapidly-Varying Features and Content

Proactive caching shows great potential to minimize peak download rates by caching popular data, in advance, at the edge. Fast-changing file features, such as fast-changing file popularities and fast-changing file contents (data freshness), represent a challenge for proactive caching if cache content update is much slower, which decreases the efficiency and usability of caching. We present a dynamic caching scheme that updates local user caches and optimizes the use of caching resources. The developed scheme index-code the updates with the delivery messages. The developed scheme is presented for a network with one cache-enabled server, that has a pool of files, communicating with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> cache-enabled receivers with requests limited to the server’s file pool. The developed scheme assumes partial knowledge of features variation. Asynchronous file delivery is assumed as a result of non-flexible receivers’ request timing. We show that the file delivery messages can be used to proactively and constantly update the receivers’ finite caches by index-coding the update messages with delivery messages at no additional rate-cost. We also show that this mechanism reduces the downloaded traffic and can be used to reduce other QoS metrics.

Index Coding Algorithms: Cooperative Caching and Delivery for F-RANs

In a Fog Radio Access Network (F-RAN), fog access points (F-APs) are equipped with caches that can store popular files during off-peak hours. Besides, they are densely deployed to have overlapping radio coverage so that requested files can be delivered cooperatively using beamforming. The bottleneck of the network is typically in the bandwidth-limited wireless fronthaul, which connects a cloud server to the F-APs. This work studies index coding design for cooperative caching and delivery in F-RAN to minimize fronthaul traffic and transmit energy. Index coding algorithms are designed considering the cached content at the F-APs and the possibility of beamforming in the access network under coded and uncoded caching schemes. An optimal polynomial-time index coding algorithm for uncoded and repetition caching and an efficient heuristic for Maximum Distance Separable (MDS) coded caching are designed, and their superior performance is verified by simulations. The study is further extended to consider the tradeoff between the traffic load of the fronthaul link and the transmit energy consumed in the access network. At the expense of more fronthaul traffic, beamforming opportunities can be increased, significantly reducing energy consumption. Algorithms to achieve the tradeoff are crafted, and simulation results show that uncoded caching well balances the tradeoff.

An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

In the current internet of things era, various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause cloud core overload and significant network delays. To overcome these overload and delay problems, the mobile edge computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, while using the service, it may also encounter a large amount of popular data being accessed in a short period of time. Without proper handling of replica generation and deployment, even in low-latency environments, it can still kill the benefits of MEC due to increased access time. Although many scholars have proposed related issues for copy replication, there are still.parts that can be improved. To avoid the situation of insufficient availability of replicas, replica replication is performed, but infinite replicas may lead to a significant increase in traffic and waste of resources. And when deploying replicas, it is necessary to avoid placing them on congested nodes and consider how to achieve better load balancing. To improve the above problems, we synthesize the advantages of previous algorithms, make up for the shortcomings, and propose an adaptive replica configuration mechanism to predict the popularity of files and replicate replicas to low-blocking nodes. This method spreads the subsequent access workload by copying the popular file in advance to improve the system's overall performance.

Fundamental Structure of Optimal Cache Placement for Coded Caching With Nonuniform Demands

This paper studies the caching system of multiple cache-enabled users with random demands. Under nonuniform file popularity, we thoroughly characterize the optimal uncoded cache placement structure for the coded caching scheme (CCS). Formulating the cache placement as an optimization problem to minimize the average delivery rate, we identify the file group structure in the optimal solution. We show that, regardless of the file popularity distribution, there are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at most three file groups</i> in the optimal cache placement, where files within a group have the same cache placement. We further characterize the complete structure of the optimal cache placement and obtain the closed-form solution in each of the three file group structures. A simple algorithm is developed to obtain the final optimal cache placement by comparing a set of candidate closed-form solutions computed in parallel. We provide insight into the file groups formed by the optimal cache placement. The optimal placement solution also indicates that coding between file groups may be explored during delivery, in contrast to the existing suboptimal file grouping schemes. Using the file group structure in the optimal cache placement for the CCS, we propose a new information-theoretic converse bound for coded caching that is tighter than the existing best one. Moreover, we characterize the file subpacketization in the CCS with the optimal cache placement solution and show that the maximum subpacketization level in the worst case scales as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ O}}(2^{K}/\sqrt {K})$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users.

Deep Learning-Enabled File Popularity-Aware Caching Replacement for Satellite-Integrated Content-Centric Networks

In recent years, satellite-integrated content centric networking (SCCN) has become an important solution for the future network with excellent bandwidth savings and file distribution capability in a wide range by the intrinsic caching function. At present, however, the high-speed changes of satellite network topology and coverage make it difficult to predict the file popularity of SCCN network, resulting in a low timeliness. This will lead to low node cache efficiency and bad data distribution performance. To address this issue, in this article, we have proposed a deep learning-enabled file popularity-aware caching replacement mechanism to achieve efficient file distribution in SCCN. In the proposed mechanism, we have developed a virtual location division scheme to keep the return path of content data invariable by remapping the time-varying topology of network into a static topology with virtual nodes. Furthermore, we have put forward a minimum delay file-caching set algorithm to predict the popularity of files in the proposed SCCN via a well-designed deep learning framework, which can find those high-popularity files most worthy of caching. The simulation results verified the proposed method can obviously degrade the access delay of all users and the cache hit ratio of satellite nodes, compared with current strategies, i.e., cache everything everywhere with least recently used, probCache, content-aware placement and discovery, and replacement (APDR), respectively.

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.

A Tunable Caching Distribution Model for Unmanned Aerial Vehicle Networks

In existing works on performance analysis of cache-enabled unmanned aerial vehicle (UAV) networks, caching distribution is fixed, and the joint cache-level and transmission-level cooperation has not been analyzed in literature. Considering UAVs agility and line-of-sight (LOS)/ non-line-of-sight (NLOS) channel characteristics, this paper proposes a tunable caching distribution model, where content placement and transmission can be flexibly adjusted to match interference topology dynamics, and derives semi-closed expression of caching coverage probability via stochastic geometry. Specifically, UAVs will take the partition coefficient ρ to determine caching distribution, where the most popular content files are reserved in every UAV within the cooperation group and jointly transmit to the user, while other files are probabilistic stored in the remaining cache space to achieve content diversity. In addition, when requiring the less popular files, user will always connect to the UAV with the best signal-to-interference (SIR) ratio among cache hit UAVs within the cooperation group. The theoretical analysis investigates the effects of various system parameters such as UAVs height, cooperative radius, and content popularity on the caching coverage probability and optimal ρ for can be found. It can be seen that optimal ρ will be decreased firstly and then increased along with the increase of UAVs height, which reveals the tradeoff between cache hit probability and transmission reliability. Analytical results show that our proposed cache and transmission policy can achieve 34% gain of coverage probability compared to the policy of probabilistic cached.